Affordable Expert Engineering Resources

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Affordable Expert Engineering ResourcesHarnessing the Power of Axys and Generative AI with SVCIT

 

Introduction:

SVCIT’s expert engineering team can connect any AI source or analytic tools in your company to generate custom insights or dashboard solutions for your company data in record time.

SVCIT: Expertise at your Fingertips

Over the years, SVCIT has built a reputation as an industry leader with its expert engineering team. With a robust background serving over 700 enterprises, their expertise and ability to deliver efficient solutions have been proven time and again. What sets SVCIT apart is not just its talent but also its ability to deploy this talent in an agile and cost-effective manner while knowing the real pains companies going through and creating a solid attempt to solve that. The result is a high-quality, fast-paced, pain-free, and affordable engineering solution for businesses of all sizes.

Democratizing Generative AI

SVCIT’s experienced team specializes in leveraging generative AI tools to create customized insights that drive business growth. These AI-based solutions are capable of performing complex tasks such as content creation, product design, predictive modeling, and risk management as well as advanced dynamic report generation. This approach not only increases efficiency and productivity but also reduces the time and cost associated with traditional methods.

The Axys Platform: Fueling Growth with DataOps

The Axys platform is another essential component of this cost-effective approach. Axys streamlines data operations, integrating different data sources into a single platform. By addressing complex aspects of data management like pipelining, indexing, and security, Axys reduces the need for substantial in-house resources.

The platform’s no-code interface enables businesses to handle data operations efficiently without the need for extensive coding knowledge. This revolutionary approach means businesses can focus on utilizing their data rather than managing it.

The SVCIT-Axys Collaboration: Affordable and Efficient

When combined, SVCIT’s engineering expertise and Axys’s efficient DataOps & Data Fabric platform make for a powerful tool for businesses. The collaboration provides an affordable solution for businesses seeking to leverage their data to its full potential.

With SVCIT’s engineers connecting AI sources to the Axys platform, businesses can quickly analyze and visualize their data. This approach leads to cost savings and time efficiencies, as businesses no longer need to rely on time-consuming and expensive in-house data analysis methods.

Additionally, the generative AI solutions implemented by SVCIT can be adapted to suit individual business needs, providing a flexible and scalable approach that grows with your business.

Conclusion:

In a world where data is king, having an affordable, extraordinarily fast development and efficient solution for managing and utilizing this data is crucial. The collaboration between SVCIT and Axys provides just that. By harnessing the power of Axys’s efficient DataOps platform and SVCIT’s expert engineering resources, businesses can not only manage their data more effectively but also gain critical insights to drive their business forward.

Whether your business is just starting to explore the world of data or is already deep in the throes of data analytics, the SVCIT-Axys solution can provide you with the tools and expertise you need to take your business to the next level.

 

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Reduce Operational RisksIntroduction:

By simplifying the complexities of managing and accessing data across multiple sources, you can reduce the need for maintaining in-house data management solutions. Our technology provides powerful data governance features that maintain data quality, security, and compliance, reducing the risk of data breaches and associated costs.

Axys: Your Key to a Seamless DataOps Environment

Axys Platform is designed to handle all aspects of data operations with finesse. It can streamline & automate the entire data management process from pipelining, indexing, and normalization to prioritization, security, and governance. With the ability to consolidate disparate data sources into a single, cohesive platform, Axys eliminates the need for constant intervention and the cumbersome handling of multiple data sets.

The Axys platform safeguards your valuable assets with its state-of-the-art industry standard security features. Addressing security, sovereignty, and governance ensures your data infrastructure remains robust, compliant, and within the company’s private network.

SVCIT: Mastering Generative AI Integration

SVCIT’s 15+ years of experience in serving 700+ enterprises, gives them an edge in understanding the nuances of enterprise-level data operations and management. Their expert engineers have excelled at integrating generative AI solutions with the Axys platform, which automates significant parts of the data operations, further reducing the need for in-house data management.

This automation leads to a reduction in human errors and biases, thereby enhancing data quality, which is pivotal for robust machine learning models. The potential risks related to data security and compliance are also significantly reduced as the process becomes more automated and less dependent on manual handling.

The Synergy: Mitigating Operational Risks

The union of Axys and SVCIT creates a solid foundation that reinforces data operations and reduces associated operational risks. With Axys’ advanced data governance and SVCIT’s expertise in integrating generative AI, companies can maintain high-quality data, ensure compliance, and enhance security without the need for a hefty in-house data management infrastructure.

Moreover, with Axys’s dynamic API layer, data access becomes easier and more efficient, reducing the risk of unauthorized access or data breaches. The seamless integration of the Axys platform and SVCIT’s generative AI capabilities empowers organizations to manage their data with increased security, compliance, and efficiency.

Conclusion:

Operational risks in the realm of data management can be detrimental to an organization’s growth and reputation. By opting for the combined power of Axys and SVCIT, organizations can mitigate these risks effectively.

With advanced data governance features, robust security measures, and seamless generative AI integration, Axys and SVCIT can help organizations maintain data quality, ensure compliance, and reduce the risk of data breaches. This synergy allows organizations to focus more on core business operations and strategic decision-making and less on managing and mitigating operational risks related to data. This strategic decision not only streamlines business operations but also gives organizations a competitive edge in the ever-evolving digital landscape.

 

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Improve Employee ProductivityIntroduction:

Your employees can easily access and analyze data from multiple sources in a single platform, reducing the time and effort required to search for and gather data. The platform also provides data visualization tools that make creating and sharing dashboards and reports easy, improving communication and collaboration among team members.

Axys: A DataOps Powerhouse

Axys, an advanced DataOps platform, revolutionizes the way businesses manage their data. By consolidating disparate data sources in a unified platform, Axys eradicates the need for employees to log into multiple applications to access data. Instead, all information is readily available in a single, user-friendly interface.

Moreover, Axys goes beyond merely simplifying data access. It also provides powerful data visualization tools, allowing employees to create and share intuitive dashboards and reports. These tools empower employees to turn raw data into meaningful insights, making data analysis more accessible and enhancing team-wide communication and collaboration.

SVCIT: Harnessing the Power of Generative AI

SVCIT, renowned for its enterprise software development and engineering services, effectively incorporates generative AI solutions into the Axys platform. Generative AI, an AI subset, uses machine learning algorithms to generate new content, predictions, or solutions, significantly contributing to the enhancement of various business operations.

By leveraging SVCIT’s vast experience and the power of generative AI, businesses can automate routine tasks, such as report generation and predictive analysis. This automation liberates employees from time-consuming, repetitive tasks, allowing them to focus on strategic operations that require their unique expertise and creativity.

The Synergy: Boosting Employee Productivity

When the capabilities of Axys and SVCIT combine, they create a potent ecosystem that promotes enhanced employee productivity. By providing easy access to data and enabling the rapid generation of actionable insights, Axys reduces the time and effort employees spend on data collection and analysis. Simultaneously, SVCIT’s implementation of generative AI automates routine tasks, allowing employees to focus their energy on tasks of higher strategic value.

Moreover, with Axys’s data visualization tools, employees can quickly understand complex data sets and share their findings with their team, enhancing intra-team communication and collaboration. This streamlined workflow reduces bottlenecks and allows projects to progress more smoothly and efficiently.

Conclusion:

In today’s fast-paced business environment, increasing employee productivity is a key determinant of success. The combination of Axys’ powerful DataOps, Data Fabric platform, and SVCIT’s expertise in implementing generative AI solutions provide a significant boost to employee productivity by simplifying data access and analysis, automating routine tasks, and enhancing communication and collaboration.

By adopting the Axys and SVCIT solution, businesses can effectively empower their employees, creating an environment where productivity thrives and innovative ideas come to fruition. This strategy not only drives the growth and success of the business but also fosters a fulfilling and stimulating work environment for the employees.

 

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Accelerate Data-Driven Decision-Making

Introduction:

Our DataOps platform uses virtualization technology to create a unified view of all data across an organization, enabling businesses to access, analyze and visualize their data more efficiently and effectively. It also provides advanced analytics capabilities to be attached to the system at any time, such as predictive modelling and machine learning to help companies to gain insights and make data-driven decisions faster.

The Power of Axys:

Axys, a state-of-the-art DataOps and Data Fabric platform, brings an innovative approach to the data management process, providing robust solutions for handling complex data operations. The platform is engineered to simplify the process of consolidating disparate data sources, enhancing data accessibility and security while reducing the development time significantly and cost associated with traditional data management practices such as resources.

One of Axys’ strongest features is its ability to empower rapid prototyping. It enables developers to experiment with various data architectures in hours rather than weeks, cutting down the complexity of data engineering and reducing backend development time. This quick iteration capability is critical for businesses that aim to stay competitive in a fast-paced, data-driven world.

SVCIT and Generative AI:

SVCIT, a leader in enterprise software development and engineering services, leverages Axys to streamline the integration process of generative AI applications. Generative AI, a form of artificial intelligence that leverages machine learning algorithms to create something new, has seen increasing adoption in various industries such as technology, healthcare, finance, and entertainment.

SVCIT’s expert engineers empower customers in integrating these AI solutions into existing systems and workflows using the Axys platform, enabling businesses to harness the power of generative AI for tasks like content creation, product design, and even customer service. By utilizing the Axys platform, SVCIT guarantees high-quality data for training these AI models, further enhancing the overall effectiveness of the generative AI solutions.

The Synergy of Axys and SVCIT:

Together, Axys and SVCIT create an ecosystem where data operations and generative AI solutions converge with the expertise of the SVCIT senior engineering team. This collaboration enables businesses to efficiently manage their data operations while simultaneously exploiting the power of AI, leading to improved productivity, enhanced decision-making, and overall business growth, as well as a significant decrease in switching costs of generative AI platforms if decided to be changed.

The unified data view provided by Axys, coupled with SVCIT’s extensive experience in generative AI implementation, paves the way for companies to unlock valuable insights from their data. These insights, in turn, foster data-driven decision-making that is faster, more informed, and more efficient.

Conclusion:

In a digital era where data is the new oil, the ability to rapidly access, analyze, and visualize data is no longer a luxury but a necessity. The innovative combination of Axys’ powerful DataOps platform and SVCIT’s proficiency in generative AI implementation provides an edge for businesses striving to remain competitive and agile in a rapidly evolving landscape.

Embrace the future of data management and AI implementation with Axys and SVCIT, and step into a world of accelerated data-driven decision-making today.

 

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Proton deployment type is a fully-managed application delivery service for container applications that enables platform operations teams to provide consistent architecture across an organization and enables developers to deliver their applications on approved infrastructure using a self-service interface. Here we are going to discuss why need AWS Proton.

AWS Proton helps to provide well-architected templates and best practices when development teams deploy containers and serverless applications.

Self-Service Interface: Allows developers to focus on shipping code, to an essential location to automate deployments. The user can see all the deployed services in a central dashboard and upgrade them to the latest infrastructure definition with one click.

Increased Control: It provides increased control over cloud infrastructure.

Work Flow of AWS ProtonAWS Proton

AWS Proton is a responsibility model. The platform team is going to create an environment template that defines shared resources and deploy environments. The platform team can also create a service template that defines infrastructure monitoring and CI/CD resources. Developers log in with their restricted access account and select the service template created by the platform team.  Developers can link their source code package and deploy their application in the target environment.

Problem to solve by AWS Proton

As there were several users have started or to had finished or were in the process of some sort of internal platform to manage all of their deployments as the amount of deployments micro-services that they were running was growing, they were trying to tackle these decisions in a way that was comprehensive and gave them control and ensure that everything was standardized. At the same time, the developers could keep moving fast.

AWS Proton features

Update a service instance

There are four modes for updating a service instance, as described below. The deployment type field defines the mode.

NONE

In this mode, deployment doesn’t occur. Only the requested metadata parameters are updated.

CURRENT_VERSION

In this mode, the service instance is deployed and updated with the new spec you provide. Only requested parameters are updated and don’t include minor or major version parameters when using this deployment type.

MINOR_VERSION

In this mode, the service instance is deployed and updated with the published, recommended (latest) minor version of the current major version in use by default. You can also specify a different minor version of the current major version in use.

MAJOR_VERSION

In this mode, the service instance is deployed and updated with the published, recommended (latest) major and minor version of the current template, y default. You can also specify a different major version higher than the major version in use and a minor version (optional).

Request Syntax

{

deploymentType“: “string”,

name“: “string”,

serviceName“: “string”,

spec“: “string”,

templateMajorVersion“: “string”,

templateMinorVersion“: “string”

}

Update an Environment

If the environment is associated with an environment account connection, do not update or include the protonServiceRoleArn parameter to update or connect to an environment an actual will account connection.

You can only update to a new environment account connection if it was created in the same environment account in which the current environment account connection was created and is associated with the current environment.

If the environment is not associated with an environment account connection, do not update or include the environmentAccountConnectionId parameter.

You can update either the environmentAccountConnectionId or protonServiceRoleArn parameter and value. You can’t update both.

Use the console or AWS CLI to make updates or cancel update deployments

Update an environment using the console as shown in the following steps.

  1. Choose 1 of the following 2 steps.
  2. In the list of environments.
  3. In the AWS Proton console
  4. choose Environments.

In the list of environments, choose the radio button to the left of the environment that you want to update.

  1. In the AWS Proton console
  1. choose Environments.
  1. In the list of environments, choose the name of the environment that you   want to update.
  1. To make an edit that doesn’t require environment deployment.
  2. For example, to change a description.
  1. Fill out the form and choose Next.
  2. Review your edit and choose Update.
  3. To make updates to metadata inputs only.
  4. Choose Actions and then Update.
  5. Fill out the form and choose Edit.
  6. Fill out the forms and choose Next until you reach the Review page.
  7. Review your updates and choose Update.
  8. To update a new minor version of its environment template.
  9. Choose Actions and then Update minor.
  10. Fill out the form and choose Next.
  11. Fill out the forms and choose Next until you reach the Review page.
  12. Review your updates and choose Update.
  13. To update a new major version of its environment template.
  14. Choose Actions and then Update major.
  15. Fill out the form and choose Next.
  16. Fill out the forms and choose Next until you reach the Review page.
  17. Review your updates and choose Update.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Knowledge representation

What is Knowledge Representation?

Knowledge Representation and Reasoning (KR, KRR) represents information from the real world for a computer to understand and then utilize it to solve complex real-life problems like communicating with human beings in natural language.

Different Types of Knowledge Represented in AI

Types of Knowledge in AI

There are five types of knowledge such as:

Declarative Knowledge: This includes concepts, facts, and objects expressed in a declarative sentence.

Structural Knowledge: It is basic problem-solving knowledge that describes the basic concepts between concepts and objects.

Procedural Knowledge: This is responsible for knowing how to do something, including rules, strategies, and procedures, etc.

Meta Knowledge: This defines the knowledge about other types of knowledge.

Heuristic Knowledge: This represents some expert knowledge in the field or subject.

The cycle of Knowledge Representation

Artificial intelligence systems usually consist of various components to display their intelligent behaviour. These components are as follows:

Here is an example to explain the different components of the system and how it works. This diagram shows the interaction of the artificial intelligence system with the real world and the components involved in showing the intelligence.

Knowledge representation

Perception: The perception component retrieves data or information from the environment. With the help of this component, the user can retrieve data from different environments, find out the source of noises and determine if the AI is damaged by anything. Also, it defines how to respond to any sync that has been detected.

Learning: This learns from the captured data by the perception component. Here the goal is to build computers that can be taught instead of programming. Learning focuses on the process of self-improvement in order to learn and understand new things; the system requirements are knowledge acquisition, inference acquisition of heuristics, fastest searches, etc.

Knowledge Representation and Reasoning: This shows the human-like intelligence in the machine. Knowledge representation is all about understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behaviour from the top down and focus on what an agent needs to know in order to behave intelligently. It also defines how automated reasoning procedures can make this knowledge available as needed.

Planning and Execution: These components depend on knowledge representation analysis and reasoning. Here planning includes giving an initial state, finding the pre-condition, effects, and a sequence of actions to achieve a state in which a particular goal holds. Once the planning is complete, the final stage is the execution of the entire process.

Relationship Between Knowledge & Intelligence

In the real world, knowledge plays an important role in intelligence as well as creating artificial intelligence. It demonstrates intelligent behaviour in AI agents or systems. Now it is possible for an agent or system to be accurate on some input only when it has the knowledge or experience about the input.

Techniques

Logic Representation: It’s a language with some definite rules which deal with propositions and has no ambiguity in representation. It proposes a conclusion based on various conditions and lays down some important communication rules.

Syntax

Semantic

Advantages

Disadvantages

Semantic Network Representation

Semantic networks work as an alternative to predicate logic for knowledge representation. In semantic networks, the user can represent their knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. This representation consists of two types of relations, such as IS-A relationship (Inheritance) and Kind-Of-Relation.

Advantages

Disadvantages

Frame Representation

A frame is a record-like structure that consists of a collection of attributes and values to describe an entity in the world. These are the AI data structures that divide knowledge into substructures by representing stereotypical situations. It’s a collection of slots and slot values of different types and sizes. Slots have been identified by names and values, which are called facets.

Advantages

Disadvantages

Production Rules

In production rules, the agent checks for the condition, and if the condition exists, then the production rule fires, and corresponding action is carried out.

Advantages

Disadvantages

Representation Requirements

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Semantic search

Search lies at the very heart of the web, with over 30 trillion websites. The web provides us with a diverse and ever-increasing amount of data, but the way we currently search for information can mean we jump from one website to another to gather all the data we need; this is because answers provided by these searches continue to direct us to be individual and isolated websites. For example, if a user wanted to find specific information, they most likely would use a web search engine that would bring up many individual websites with only parts of the answers to their query. The world of information that they contained would become unnavigable.

So the user will end up comparing copying and pasting results from their search engines, and from social networking sites, etc.; endless to gather all the information they require combine it and draw conclusions from their past search experiences or according to user preferences because the search engine does not know what they like or what in user area means. Searching in this way does not allow users to search all these sites with just one question. Each of these websites is built using different standards and stores its information differently; that is what a search engine cannot understand. Here we are going to describe the semantic search and its uses.

What is Semantic Search?

Why Use Semantic Search?

How does it work?

How can Semantic technology help to improve information retrieval?

The lack of harmonization hinders the power of the internet as one vast base of knowledge. What is needed is that your computer can answer the question without visiting all these websites. This requires websites to have some extra information called resource descriptions for micro-formats which their computer understands. These formats are embedded in the internal structure of the website, also called the HTML markup.

The resource descriptions and microformats tell automated programs that a web page talks about a person’s events, musicians, etc. The text block on the site results from users’ specific search queries. This will help those programs trawl through websites to easily collect, compare, and select all the information needed and draw conclusions using automated reasoning techniques.

More and more newspaper sites, encyclopedias like Wikipedia, movies, database music portals, event sites, and personal websites provide this helpful extra information to guide computers when finding answers to users’ questions.

Prototype Search Engine

Prototype search engines such as Cindy J and Sigma collect and index this information and allow users to question the web. Users‘ computer will translate these to a language called sparkle which is the standard query language that enables users to formulate structure questions that a computer can answer for users’ queries by matching users’ query to those sites on the web which provides the relevant extra information, combined with the information available on users’ computer. With Semantic search, users don’t need to spend their time searching through lots of websites to get an appropriate result.

Semantic Search Vs. Keyword-Based Search

Query String Refinement: Enable more precise or more complete search results.

Cross Referencing: Enable to complement search results with additional associated or similar information.

Fuzzy Search: Enables the determination of nearby results and related content.

Exploratory Search: Enable visualization and navigation of the search space.

Reasoning: Enable to complement search results with implicitly given information.

Retrieving the Result Based on the Context

There are multiple ways to get the data based on the query.

Different Ways of User Input  

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Predictive Analysis

What is Predictive Analysis?

Predictive analysis is the branch of data analysis that is mainly used to predict future events or outcomes. It is solely based on data-driven approaches and techniques to reach conclusions or solutions.  The analysis mainly uses analytical techniques and predictive modelling to find relevant patterns in large data sets; in turn, these patterns can be used to make various opportunities in businesses by identifying the risk and benefits. The predictive Modeling Technique is an anticipatory technique for forward-looking solutions and insights to assess any situation.

Most of the processes in predictive analysis incorporate machine learning terminologies and algorithms for model building, especially to train the models.

How to Choose a Correct Predictive Technique?

It is significantly important to understand how to choose the correct predictive technique for model building.

Predictive Analysis Techniques

Regression

The primary role of regression is the construction of an efficient model to predict the dependent attributes from a bunch of attribute variables. A regression problem is when the output variable is either real or a continuous variable that can be weighed, area, or salary. Regression can also be defined as a statistical means that is used in applications like housing investing etc.

It is used to predict the relationship between a dependent variable and a bunch of independent variables and a simple linear regression technique in which the independent variable has a linear relationship with the dependent variable. It is a technique to analyze a data set with the dependent variable and one or more independent variables to predict the outcome in a binary variable, meaning it will have only two outcomes, and the dependent variable is categorical.

Logistic Regression

It is a special case of linear regression where only one needs to predict the outcome in a categorical variable. It predicts the probability of the event using the log function.

Classification

Classification is a process of a given set of data into classes, and it can be performed on both structured or unstructured data. The process starts with predicting the class of given data points, and classes are often referred to as target labels or categories. The classification predictive modelling approximates the mapping function from input variables to discrete output variables. The main goal is to identify which class or category where the new data will fit into.  For example, heart disease detection can be identified as a classification problem, and it’s a binary classification since there can only be two classes with heart disease or do not have heart disease.

So, in this case, the classifier needs training data to understand how the given input variables are related to the class. Once the classifier is trained accurately, it can detect whether heart disease is there or not for a particular patient since the classifier is also a type of supervised learning; even the targets are also provided with the input data.

Clustering

Clustering means dividing data points into homogeneous classes or clusters. The points in the same group are as similar as possible, and then the points in different groups are as dissimilar as possible. So, when a collection of objects is given, the object will be grouped based on similarity.

Time Series Model

The Time series model comprises a sequence of data points captured using time as the input parameter. It uses the last year of data or previous data to develop a numerical metric. It predicts the data using that metric to understand a singular metric is developing over time with a level of accuracy beyond simple averages.

Forecasting

Forecasting is nothing but using historical data to make predictions or numeric predictions on new data based on the learning from the previous.

Choosing the Predictive Analysis Technique

Before choosing the best predictive analysis technique for a project, first understand some important points such as:

Problem Statement

Before building a model, first, we need to understand the problem statement, which helps to understand what kind of target result is required. For example, we have a problem statement to decide if a patient has heart disease or not.

So this problem statement is categorical, and it will have only two values: one has heart disease or does not have heart disease. In this particular example, we can use the classification technique to model this data, but there are a few problems where it is difficult to choose a target variable.

Target Variable

If the target variable is continuous, we can choose regression analysis, and if the target variable is categorical, we can use classification analysis. And if the target variable is identified we can also go for clustering analysis.

Linearly Separable Data

There is no direct way to determine linearly separable data; we can determine it by choosing different models or comparing them.

Size of The Data

The size of the data helps to determine the possibility of overfitting and underfitting a model. Also,   some models may not work efficiently with small data, so these are some deciding factors for choosing a model before training a model with the training data.

Machine Learning Models for Predictive Analysis

Linear Regression

Linear regression is to be used when a target variable is continuous, and the dependent variable or variables are continuous or a mixture of continuous and categorical. The relationship between the independent variable and the dependent variable has to be linear.

Logistic Regression

It does not require a linear relationship between the target variable and the dependent variables. The target variable is the binary assuming a value of either 1 or 0.

Neural Networks

The Neural networks help to understand or help to cluster and classify the data.

K-Means Clustering

K-Means involves placing unlabeled data points in separate groups based on similarities, and this algorithm is used for the clustering model.

Decision Trees

The decision tree is a map of possible outcomes of a series of related choices; it allows an individual or organization to weigh possible actions against one another based on their costs, probabilities, and benefits.  It is useful to drive informal discussion or map out an algorithm that predicts the best choice mathematically.

Time Series

The time series regression analysis is a method for predicting future responses based on response history. The data for a time series should be a set of observations on the values that a variable takes at different points in time.

Predictive Analysis Applications

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Recommender System

What is a Recommender System?

A recommender system is an unsupervised machine-learning algorithm. It’s an automated system to filter some entities. These entities can be any products, ads, people, movies, or songs, and we see this technology at Amazon to Netflix, Pandora, YouTube, and eHarmony. For example, when a user watches a movie, they will get recommendations for other related movies on their screen based on the power of previous viewing history. It can also be a product that users bought, and then they will get a recommendation for another product based on the last viewing product or purchase history.

A recommender system can build either by finding questions that a user may be interested in answering, or based on the questions answered by other users like him finding other questions similar to the questions he answered already.

The recommender doesn’t work only in what products we are being shown and in what order the products are being ranked. It is an effective technique in terms of business because Google, Facebook, and Amazon are all big companies using powerful recommendation systems to expand their business by determining the users’ interests.

Why Need a Recommender System Built?

Businesses are showing us recommendations and relevant content for a couple of reasons. Most businesses think they understand their customer, but often customers can behave much differently than they would think. Hence, it’s essential to show the users what is relevant to them while also sharing new items they would be interested in.

Recommender systems also help solve the information overload problem and help us narrow down the set of choices. For businesses, they benefit from selling more relevant items to the user.

It also helps customers to discover new and interesting things and help to save time. From a business perspective, it helps to understand better what the user wants. Similarly, user reviews, ratings, and relevancy can play a factor in terms of what is being recommended to customers.

How Does the Recommender System Work?

When a customer purchases a product online, the recommendation engine will ask the customer what they want or ask if the content is relevant, look at another user with similar behaviour, or study the customer’s activity.

For example, when a user goes to Netflix or any other service that relies on recommendations, the first time when a user visits there they will ask what their taste preferences are, and there is a reason for that; if they do not know what is their customers taste preferences are at all, they have no idea what their customers’ need. They have no profile for the customer. It’s a “Cold Start Problem.”

Types of Recommender Systems

There are three types of recommender systems such as:

Content-Based Filtering

Recommend items based on the browsing or purchase history in the past or based on the content of items rather than other users’ opinions.

User Profiles: Create user profiles to describe the types of items that the user prefers (e.g., correlations among items).

Recommendations based on keywords are also classified as content-based.

Advantages

Limitations

Collaborative Filtering

Recommend items based on the interests of a community of users. This method finds a subset of users who have similar tastes and preferences to the target user recommendations.

Basic Assumptions

Main Approaches

There are two main approaches in collaborative filtering: User-Based filtering and Item-Based Filtering.

User-Based

Advantages

Problems

Sparsity Problem

If there are many items to be recommended, the user/ rating matrix is sparse, and it is hard to find the users who have rated the same item.

Popularity Bias

Tend to recommend only popular items.

Item-Based

Advantages

Problems

Hybrid Content-Based Collaborative Filtering

Hybrid is a combination, content-based, and collaborative filtering approach to overcome the disadvantages of each approach.

Recommendation Algorithms

Recommendation Engines

Implementation

Key Parameters

Key Features

Generic user-based recommender ·         User similarity metric.

·         Neighborhood definition and size.

·         Conventional implementation.

·         Fast when the number of users is relatively small.

Generic Item Based Recommender ·         Item similarity metric. ·         Fast when the number of items is relatively small.

·         Useful when an external notation of item similarity is available.

Slop One Recommender ·         Different storage strategies. ·         Recommendations and updates are fast at runtime.

·         It requires a large re-computation.

·         Suitable when the number of items is relatively small.

SVD Recommender ·         Number of Features ·         Good results.

·         It requires large pre computations.

KNN Item Based Recommender ·         Number of means (k)

·         Item similarity metric.

·         Neighborhood size.

·         Recommendations are fast at runtime.

·         It requires large precomputation.

·         Good when the number of users is relatively small.

 

Non-Personalized Recommendation

As a person begins to browse a few pages, the engine determines a person’s preferences and leverages this information to offer tailored recommendations.

Data Acquisition

Processing Model of Recommendation Engine

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Ansible

Why Ansible?

Ansible is a DevOps environments tool for helping manage the service and falls on the operations side of the DevOps equation. It allows for maintaining all the different servers such as web servers running Apache, and database servers running MySQL. It isn’t easy to maintain many servers by hand simultaneously here. Ansible helps become an efficient operation.

Like other system solutions such as Chef and puppet, Ansible uses code to write and describe the installation and set up multiple servers.

What is Ansible?

Ansible is an IT automation, configuration management, and provisioning tool. It makes sure that all the necessary packages and software are downloaded and installed in the system to run the application. Ansible is a tool that allows us to create and control three key areas that are useful within the operational environment such as:

IT Automation: The user can write instructions that automate the IT setup that the user would typically do manually in the past.

Configuration Management: Provides consistent configuration; imagine setting up hundreds of Apache servers and guaranteeing with precision that each of those Apache servers is set up identically.

Automatic Deployment: As users need to scale up their server environment, they will just need to push instructions to deploy different servers.

Pull Configuration Tool: There are two different ways for Ansible to set up different environments for server farms. One way is to have a key server that has all the instructions on and then on each of the servers that connect to that main master server; it provides a piece of software known as a client on each of those servers that would communicate to the main master server and then would periodically either update or change the configuration of the slave server; this is known as pull configuration.

Its alternative is push configuration which is slightly different; the main difference is as with pull configuration has a master server where the user can put the instructions, but unlike the pull configuration where the user needs a client installed on each service with a push configuration, the user has no client stored on the remote server.

The user will push out the configuration to the servers and force a restructure or a fresh, clean installation in that environment.  So, Ansible is one of those second, advanced ways to push configuration servers.

Configuration Management

Features of Ansible

Push Based Vs. Pull Based

Tools like Puppet and Chef Are Pull-based. Here agents on the server periodically check for the configuration information from a central server (Master).

Ansible is push based on its central server pushing the configuration information on target servers. The user can also control when the changes are made on the servers.

Highly Scalable & Available

It can scale up to 1000’s on nodes. And runs with a single active node, called the primary instance; if the primary goes down, there is a second instance to take its place.

Ansible Playbook

The playbook is a core part of Ansible, whatever configuration details that users write in the playbook. The entire IT infrastructure gets automated by using a playbook. The playbooks are written in YAML code which is a very simple data serialization language. It is very human-readable and almost like English.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

The Cassandra vs. MongoDB vs. HBase

Big data is revolutionizing the world of the IT industry, and according to Forbes, analysts estimate upward of 80% of enterprise data is unstructured. Unstructured data cannot always be handled in real-time if an organization tries to store this data and their DBMS, it will be difficult to scale up the data in real-time to get better performance. Here we are discussing Cassandra vs. MongoDB vs. HBase.  First, we need to understand what a NoSQL database is.

NoSQL, which stands for “Not Only SQL,” is an alternative to a traditional relational database. Data is placed in a table, and the data schema is carefully designed before the database is built.

Why need No SQL database?

As compared to relational databases, NoSQL is more scalable and provides superior performance. NoSQL databases provide the following solutions such as:

Cassandra vs. MongoDB vs. HBase

Types of NoSQL Database

There are four types of databases such as:

Key-Value Store

It has a big hash table of keys, and values, for example, Amazon S3.

Column Based Store

In this case, each storage block contains data from only one column, like Cassandra and HBase.

Document-Based Store

It stores the document that is made up of tag elements, for example, CouchDB or MongoDB.

Graph-Based Store

In this case, a network database uses edges and knows to represent and store the data, for example, Neo4j.

Apache Cassandra

Apache Cassandra is the leading no-sequel distributed data management system that drives many of today’s modern business applications by offering continuous availability, high scalability, performance, strong security, and operational simplicity by lowering the overall cost of ownership.

Data Model of Cassandra

Key Spaces

Cassandra is a white-column store model based on the ideas of BigTable and the dynamo database. Moreover, it consists of keyspaces, the outer container in Cassandra, and the column family contains an ordered collection of rows.

Implementation Language

Cassandra’s implementation can implement by using one of the most popular object-oriented programming languages called Java.

Query Language

Cassandra uses its query language Called Cassandra query language.

Security

MongoDB

It is a documented-oriented database. All the data in MongoDB is traded in JSON format, and it is a schema-less database that goes over terabytes of data in the database.

Data Model of MongoDB

Flexible Schema

MongoDB is a document storage architecture with data. MongoDB also has a flexible schema document in the collection. It doesn’t need to have the same set of structure fields, but common fields in the collections document may hold different types of data.

Implementation

MongoDB can implement using C++ programming language through these databases’ implementation using object-oriented concepts. It also provides wide support to all other programming languages.

Query Language

MongoDB is queried using dynamic object-based language and Javascript.

Security

Apache HBase

Apache HBase is a no sequel key-value store which runs at the top of HDFS. Unlike high HBase, operations run in real-time on its database rather than the Map Reduce jobs.

Data Model of Apache HBase

Column Oriented Database

It is partitioned into tables, and tables are future split into column families. Column families must be declared in the schema and grouped by a certain set of columns so that columns don’t require schema definition, and HBase works by storing data like keys and values.

Implementation Language

Cassandra is implemented by using one of the most popular object-oriented programming languages called Java.

Query Language

HBase uses the query language MapReduce.

Security

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Neo4J with Apache Kafka

Apache Kafka

Apache Kafka is a distributed stream platform built on three capabilities:

How does Apache Kafka Works?

Topics: A topic is a category or feed name to which records can publish.

Partitions: For each topic, the Kafka cluster maintains a partitioned, distributed persistent log.

How is Apache Kafka Used?

Organizations generally use Kafka for two classes of applications:

What are Neo4j Streams?

It’s a Neo4j plugin that enables Kafka to stream on Neo4j. The project is composed of two plugins. The first one is the Neo4j plugin that must be installed in Neo4j. It provides three features such as:

It also provides Kafka connect sink plugin. If the user chooses Kafka to connect the plugin, then it only provides the sink module.

Benefits of Neo4j Integration with Kafka

Neo4j – Kafka Integration – Use Cases

How can it be used?

Neo4j Streams: Change Data Capture (CDC)

What is CDC?

In the database, Change Data Capture (CDC) is a set of software design patterns used to determine (and track) the data that has changed so action can be taken using the changed data.

How CDC Works?

Each transaction communicates its changes to our event listener:

Neo4j Stream: Sink

Ingest Data

The sink provides several ways to ingest data from Kafka, such as:

How does Neo4j Stream Manage Bad Data?

The Neo4j streams sink module provides a dead letter queue mechanism that, if activated, re-routes all “bad-data” to a configured topic.

 Neo4j Stream: Procedures

The user can directly interact with Apache Kafka from Cypher. The Neo4j stream project comes out with two procedures:

Streams-Publish: Allows custom message streaming from Neo4j to the configured environment by using the underlying configured producer.

Stream, consume: This allows consuming messages from the given topic.

Confluent Connect Neo4j Plugin

The second plugin is the Kafka Connect plugin. It is an open-source component of Apache Kafka that connects Kafka with external systems such as databases, key-value stores, search indexes, file systems like HTFS, etc.

It works exactly like the Neo4j Sink plugin to provide each topic for the users’ ingestion setup.

UseCase

Real-time Polyglot Persistence with Elastic, Kafka, and Neo4j

First, we need to ingest data into Elastic and Neo4j from a Kafka topic. Then, the user needs to prepare a fake data generator that emits records to Kafka. It allows emitting records over two topics: one for personal info records and one for movies.

Elastic, Kafka, and Neo4j

First, it will create a fake database; suppose the fake database is based on famous movies recorded in the Neo4j ecosystem, and from the same two topics, we will ingest data. Finally, we will ingest the data as a graph into Neo4j, and we will ingest the data into Elastic as indexes.

In the second step, the user needs to use Neo4j to run the page rank over the graph. To find the most influential actors over the network and the result, the scope used by this page rank computed via the graph algorithms library are published to a new Kafka topic. So, on the other side, the Kafka sink that insists on a new topic will update the data into Elastic with the score of the page rank computation because we can provide a search feature for instances where the records are recorded by the page rank computation.

Neo4j emitting data to Elastic through Kafka

 (Neo4j emitting data to Elastic through Kafka )

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon Neptune

Amazon Neptune- Fully Managed Graph Database

Amazon Neptune is a fully-managed graph database service. It’s a fast service, and its design is specifically for graph applications that need to have high throughput query answering with low latency. So the Neptune users can query billions of relationships with millisecond latency.

It is designed to be reliable. Amazon Neptune also offers multi-az, high availability supports further horizontal scaling through reading replication, and also supports full encryption at rest.

It has the enterprise features that customers typically need to put a graph database into production, and it’s easy to use by supporting the most commonly used graph models, the property graphs, and the RDF models and providing support for gremlin and sparkle.

Billions of Relationships: It is optimized to store billions of relationships and query the graph with million-second latency.

W3C RDF: It supports famous graph models, property graphs, W3C, RDF, their respective query languages, apache tinker pop gremlin, and sparkle so, it’s easy to build queries that efficiently navigate highly connected data sets.

Fully Managed Services of Neptune

Advantages of Neptune

Advantages of Neo4j

Amazon Neptune Vs. Neo4j

Core Features

Amazon Neptune

Neo4j

Description AWS Neptune is a fast, reliable graph database built for the cloud. Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings
Primary database model Graph DBMS, RDF store Graph DBMS
Developer Amazon Neo4j, Inc
Server Operating System Hosted Linux OS, X Solaris Windows
Secondary Indexes No Yes
SQL NO NO

 

Why need a Graph Database?

AWS Neptune Vs. Neo4j – High Availability and Replication

AWS Neptune

Neo4j

Amazon Neptune divides data into 10GB “chunks” spread across many disks. Each chunk is replicated six ways across three availability zones. Loss of up to two copies does not affect writes, while the loss of up to three copies does affect reads. Neo4j instances have master-slave cluster replication in high availability (HA) mode. A Master maintains a master copy of each data object and replicates this to each Slave (the full dataset is replicated across the entire cluster).
Neptune supports up to 15 read replicas at a time to replicate asynchronously with automated failovers (replica instances share similar underlying storage as the primary instance). Updates are typically made from the master, which has no regard for the number of instances that fail as long as it remains available.
AWS Neptune is not supporting cross-region replicas; it allows for prioritizing and modifying as failover targets by assigning a promotion priority. Neo4j doesn’t have a master- replication, and there is no way to set master priority for instances.
Amazon Neptune provides high availability boils down to the number of replicas and their priority tiers. Although writes are synced with the elected master, reads can be done locally on each child, which means read capacity increases linearly with instances.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Apache Kafka Vs. Amazon Streaming

What is Apache Kafka?

Apache Kafka is an open-source stream-processing software platform developed by LinkedIn to donate to Apache Software Foundation and written in Scala and Java. Kafka architecture is made up of topics, producers, consumers, consumer groups, clusters, brokers, partitions, replicas, leaders, and followers. Kafka cluster consists of one or more Kafka brokers running Kafka. Producers are processes that push records into Kafka topics within the broker. A consumer pulls records off a Kafka topic. Topics are divided into partitions, and these partitions are replicated across brokers. Each partition includes one leader replica and zero or greater follower replicas.

Zookeeper performs the management of the brokers in the cluster. We can use multiple Zookeepers in a cluster at a time, for example, three to five.

Here we are going to discuss Apache Kafka Vs. Amazon Managed Streaming.

Apache Kafka Core APIs

Apache Kafka has five core APIs:

Key Benefits of Apache Kafka

Challenges Operating Apache Kafka

 

Amazon Managed Streaming for Apache Kafka

Amazon Managed Streaming for Apache Kafka (MSK) has the following components:

Key Benefits of AWS MSK

Key benefits of AWS Managed Streaming for Apache Kafka (MSK):

AWS MSK Deployment with Kubernetes

It can deploy and scale via any Kubernetes environment such as AWS EKS or users’ existing Kafka Connect cluster.

Pros and Cons of AWS MSK

Pros

Cons

Pricing Model Comparison

Apache Kafka

AWS MSK Pricing

 

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon SageMaker

Amazon SageMaker is a cloud machine-learning platform that helps users build, train, and deploy machine-learning models in a production-ready hosted environment. Amazon SageMaker helps data scientists and developers to prepare data and build, train and deploy machine learning models quickly by bringing together purpose-built capabilities. These capabilities allow users to build highly accurate models that improve over time without all the undifferentiated heavy lifting of managing ML environments and infrastructure.

What does AWS SageMaker Do?

Benefits of Using AWS SageMaker

Machine Learning with AWS SageMaker

Traditional machine learning development is a complex iterative process amazon SageMaker studio solves this challenge by providing all the tools needed to build, train and deploy models. We need to create a machine learning model to predict the cost of cars, data containing a number of models, details to predict sale prices; all this data can be put into a CSV file and then drop into an Amazon S3 bucket. Then the user can launch SageMaker autopilot and spin up models with different algorithms, datasets, and parameters. Iteratively trains dozens of models at once and then puts the best sets on a leaderboard accuracy.

In addition, the user can dive into any of these individual models and inspect their features and then deploy the best one according to their use case with a single click after deployment. The user can oversee model quality using the Amazon SageMaker model monitor at any point.  If problems are detected, the user will receive an alert to retrain the model as needed with Amazon SageMaker studio.

It also allows for pulling the tool used in traditional software development such as debuggers and profilers into a single pane of glass to build, train and deploy machine learning models at scale.

Build

Test and Tune

How to Validate a Model?

The user can evaluate their model using offline or historical data.

Offline Testing: Use historical data to send requests to the model through Jupyter notebook in Amazon SageMaker for evaluation.

Online Testing with Live Data: It deploys multiple models into the endpoint of Amazon SageMaker and directs live traffic to the model for validation.

Validating using a “holdout set”: Here, a part of the data is set aside, which is called “holdout set,” setup data is not used for model training. Later, the model is trained with the remaining input data and generalizes the data based on what it learned initially.

k-fold validation: Here, the input data is split into two parts. One part is called k-fold, which is the validation data for testing the model, and the other part, K-1, is used as training data. Now, based on the input data, the machine learning models evaluate the final output.

Companies Using AWS SageMaker

Built-In Algorithms

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

AWS Data Pipeline Service

Data is growing exponentially at a rapid pace. Companies of all sizes realize that managing this data is a more complicated and time-consuming process.

Problem Statement

Massive amounts of data are in different formats, so processing, storing, and migrating data becomes complex. Companies have to manage various types of data such as:

Depending on the need for data, maybe companies store their data at different data stores. They may store real-time data into DynamoDB, bulk data in Amazon S3, and store sensitive information in Amazon RDS. So processing, storing, and migrating data from multiple sources become more complex.

Feasible Solution

The feasible solution is to use different tools to process, transform, analyze and migrate the data.

Optimal Solution

The optimal solution for this problem is a data pipeline that handles processing, visualization, and migration. Data pipeline also makes it easy for the users to integrate that is spread across different data sources. It also transforms, processes, and analyses data for the company at the same location.

AWS Data Pipeline

Amazon web services offer a data pipeline service called AWS data pipeline. AWS data pipeline is a web service that helps users reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. It helps to easily access data from various locations, transform and process it at scale, and then transfer the results to the other AWS services such as S3, Dynamodb, or its cloud on-premise data store.

Using AWS Data Pipeline, the user can archive their web server logs to the Amazon S3 bucket daily and then run the EMR cluster over these logs to generate on a weekly basis.

The data pipeline concept is straightforward. To begin with, the user needs AWS Data Pipeline sitting on top, input data stores like Amazon S3, Redshift, or Dynamodb, etc. Data from these data stores is pass to the AWS pipeline, where it will process, analyze, transform according to user need; analysis results are put into data stores. The output data stores can be S3 bucket, Dynamodb table, etc.

Benefits of AWS Data Pipeline

Components of AWS Data Pipeline

It has three components to work together to manage data.

1.    Pipeline Definition

An organization has to communicate business logic to the AWS Data Pipeline service.

Data Nodes: It contains different services, such as it has data nodes; the data nodes are nothing but the names, locations, and formats of data sources.

Activities: It also has activities that transform the data, such as moving data from one source to another or performing queries on the data.

Schedules: The user can schedule their activities.

Preconditions: The user also sets preconditions that must be satisfied before scheduling their activities.

Resources: It has computed resources like Amazon EC2 instances or Amazon EMR clusters.

Actions: Actions update users about the status of their data pipeline. For example, it will send a notification to their email or trigger an alarm, etc.

2.    Pipeline

Pipeline offers to schedule and runs tasks to perform defined activities. It has three-part such as:

3.    Task Runner

Task runner is an application that pulls AWS Data Pipeline for tasks and then performs those tasks.

AWS Data Pipeline Service

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Here we are going to discuss how Kibana works in the ELK stack. But, first, we need to understand the ELK stack.

ELK stack combines three open-source tools: ElasticSearch, Logstash, and Kibana for log analysis. Logs are one of the most important pieces of data. Kibana uses the excellent faceted queries as provided by ElasticSearch to create tables, histograms, pie charts, and maps with geo points.

ElasticSearch: An Apache Lucene-based search engine; it’s an open-source tool and developed in java. It’s a database that holds companies’ data and allows analysis and searches on data. It stores data in the form of indexes.

Logstash:  Logstash is responsible for getting data from multiple sources to that particular index.

Kibana: Here Kibana, is the most integral part because ElasticSearch can do searches and analysis, but it does not have a UI. Here Kibana is a tool that provides the user interface to the ELK stack. The user will search Kibana, and Kibana goes and searches ElasticSearch for that particular data. It is also helpful for log, time-series analytics, application monitoring, and operational intelligence.

Roles of Kibana in ELK

Companies using Kibana

A lot of popular companies are using Kibana, such as:

Kibana Dashboards

In the ELK stack, Kibana allows us to create visualization analyses; the dashboards are just JSON documents. There are two ways to design a dashboard in Kibana, i.e., storing these JSON documents in ElasticSearch, and creating a template, i.e., a JSON document based on a specific schema. By default, each dashboard can consist of the following items: services, rows, panels, and index. The services can reuse between different panels simultaneously. Here rows are the objects that contain all rows with panels. The user can add multiple panels to their dashboards freely according to their needs, such as a table, histogram, terms, text, map, etc.

It has support for creating dashboards dynamically via templates and advanced scripts. It allows its users to create a based dashboard and then influence it with parameters. Templates and scripts must be stored on a disk, and they must be created by editing or creating a schema.

 

Kibana Custom Dashboard Creating Drilldowns

Custom dashboard actions, or Drilldowns, allow us to create workflows for analyzing and troubleshooting our data. Drilldowns apply only to the panel user created the Drilldown from and are not shared across all panels. Each panel can have multiple Drilldowns. Kibana supports dashboard and URL Drilldowns.

Dashboard Drilldowns

Dashboard Drilldown allows us to open a dashboard from another dashboard, taking the time range, filters, and other parameters to remain in the same context. For example, suppose a user wants to show the overall status of multiple data centers. That case can create a Drilldown that navigates from the overall status dashboard to a dashboard that shows a single data center.

URL Drilldowns

Using URL Drilldowns, the user can navigate from a dashboard to an internal or external URL. For example, suppose a user wants to create a dynamic URL Drill Down as if they have a dashboard that shows data from the GitHub repository. In that case, that can generate a URL Drilldown that opens GitHub from the dashboard panel.

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Azure Blob Storage

Azure Blob storage is a service for storing many unstructured object data, such as text or binary data. Blob storage can also be used to expose data publicly to the world or store application data privately.

Common uses of Blob storage include:

Why Azure Blob Storage

Companies that are using their data effectively are generating a competitive advantage in this era of data. But for most organizations, data comes from many different sources and can quickly create silos. These silos are expensive to store and a challenge to manage as most of the data being generated is unstructured and growing faster.

To stay current organizations, need comprehensive support for unstructured data workloads on a single modern platform here; Azure Blob storage helps store a massive amount of unstructured data inexpensively.

Features of Azure Blob Storage

Azure blobs allow users to meet any capacity requirements, protect data and manage storage with ease.  Azure blob allows organizations to store binary and application data, videos, audio files, and text. Blob storage is built from the ground up to support the scale, security, and availability requirements needed by mobile, web, and cloud-native application developers.

Storage Account

Access to Azure storage is done through a storage account. This account can be general-purpose or a Blob storage account.

Storage Containers

Blob

o   Block Blobs Storage

o   Page Blobs

o   Append Blobs

Azure Storage: Security

Types of Azure Blob

Block Blobs

Append Blobs

Page Blob

Blob Storage Access Tier

Azure storage provides different options for accessing block blob data based on usage patterns.

Hot: Optimized for frequent access to objects.

Cool: Blob storage allows users to optimize for storing a large amount of data for infrequent access to data and storage for at least 30 days.

Archive: Optimized for data that can tolerate several hours of retrieval latency and remain in the archive tier for at least 180 days.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

AWS vs. Azure vs. GCP

Here we are going to discuss AWS vs. Azure vs. GCP. How are these three cloud services different, and what factors do we need to focus on while comparing Amazon web services, Azure, and Google Cloud Platform?

AWS vs. Azure vs. GCP

Amazon Web Services

AWS is the oldest and the most experienced player in the market, as it was established at the beginning of 2006. AWS has an extensive list of computing services with deployment functions, mobile networking, databases, storage, security, etc.

Microsoft Azure 

Azure was presented in February 2010, and since then, it has shown great promise among its rivals. This platform can easily be associated with AWS and provide their customers with a full set of services in compute, storage, database, networking, and many more.

Google Cloud Platform (GCP)

The Google Cloud Platform (GCP) began its journey on October 6, 2011, and by this time, they have managed to create a good presence in the industry. Initially, the push was to strengthen their services, such as Google, YouTube, and Enterprise services.

Availability Zones

Availability zones are the isolated locations within data center regions from which public cloud services originate and operate. The regions are geographic locations in the data centers of public cloud service providers. Businesses using the cloud choose multiple worldwide availability zones for their services depending on their business needs as companies select the availability zones for various reasons, including compliance and provide proximities to end customers. The cloud administrator can also choose to replicate service across multiple availability zones to decrease latency and protect their resources.

Admins can move resources to another availability zone in the event of a blackout.

Companies Using AWS

Companies Using Azure

Companies Using GCP

Services

AWS covers 200+ services, Azure covers 100+, whereas Google Cloud has been catching up with it, with several 60+ services.

Primary Services of AWS Vs. Azure Vs. GCP

All these three services also help users launch an instance on the cloud, like running a virtual machine or an operating system without an on-premise infrastructure.

Downtime

Amazon Web Service Downtime: Hence, having a mature infrastructure, the maximum downtime faced by AWS in 2014 was of 2 hours and 69 minutes.

Microsoft Azure: Azure faced a huge downtime of 39 hours and 77 minutes in 2014.

Google Cloud Platform: GCP faced a downtime of only 14 minutes in 2014.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Spark Databricks Vs. Synapse Analytics

Spark Databricks Vs. Synapse Analytics

Spark is a cool open-source big data processing platform that can revolutionize everything we are doing in building analytics platforms. Here we are discussing the big beatdown that is Spark Databricks Vs. Synapse Analytics.

Databricks

Databricks is cross-platform, and that’s an important piece; if users build a ton of scripts out using data bricks, they can have the option to port that to Amazon in the future. So they have quite close parity in terms of the two versions working across them; it has its runtime, so the guys in Databricks can contribute 70% to 80% of the content that goes into the Spark open-source project comes from Databricks.

Special Skills

Pros of Databricks

Cons of Databricks

Azure Databricks Workspace

Synapse Analytics

Special Skills

 Synapse Dedicated SQL Pools

Pros of Synapse

Cons of Synapse

When to Use Synapse or Databricks?

Scenario Preferred
Ad-hoc data lake discovery by code. Synapse and Databricks
SQL analyses and Data warehousing Synapse
Same data, data scientists play via Spark and data analysts play via SQL and BI use power BI Synapse
More ML / AI development, GPU intensive tasks Databricks
Dependent tech is much on Data lake format / Spark Databricks
In-built GIT based developer experience Databricks

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

AWS DevOps Vs. Azure DevOps

AWS DevOps Vs. Azure DevOps

What is DevOps?

DevOps is a set of software development practices that combines development (Dev) and operations (Ops) to shorten the SDLC while delivering feature fixes and updates frequently in close alignment with business objectives.

Here we are discussing AWS DevOps Vs. Azure DevOps.

What is AWS?

AWS which stands for Amazon web services is an “Amazon.com” subsidiary that offers cloud computing services like:

AWS DevOps Tools

What is Azure?

Microsoft Azure is commonly referred to as Azure. Microsoft Azure is a cloud computing platform created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers.

Azure DevOps Tools

 

AWS DevOps Vs. Azure DevOps

AWS DevOps Azure DevOps
AWS DevOps provides a continuous delivery service for fast and reliable application updates. Azure DevOps’s services for teams to share code, track work, and ship software.
Continuous Deployment It provides Integrated development environment tools.
Features:

·         Workflow Modeling

·         AWS Integration

·         Pre-built Plugins

Features:

·         Agile Tool

·         Reporting

·         Git

Simple to setup Difficult to setup
Uses:

·         Simple to setup

·         Managed Services

·         GitHub Integration

·         Parallel Execution

·         Automatic Deployment

·         Manual Steps Available

Uses:

·         Open-Source

·         Several Integrations

·         GitHub Integration

·         Project Management Features

·         Jenkins Integration

·         Free for Stockholders

 

Integration Tools:

·         GitHub

·         Jenkins

·         Amazon EC2

·         Amazon S3

·         AWS Elastic Beanstalk

·         Run Scope

·         CloudBase

Integration Tools:

·         GitHub

·         Git

·         Docker

·         Slack

·         Jenkins

·         Trello

·         Visual Studio

AWS DevOps is a set of developer tools that allows the user to create a CI/CD pipeline from the source to the deploy stage. Azure DevOps is a tool provided by Microsoft Azure, which helps implement the DevOps life cycle in a business.
AWS DevOps can easily automate a complete code deployment with AWS services. Azure DevOps has Kanban boards, workflows, and a huge extension ecosystem.

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Create Kafka Streams using Spring Cloud Stream

How to Create Kafka Streams using Spring Cloud Stream?

In this blog, we will discuss how we can create Kafka streams using Spring Cloud. Apache Kafka needs no introduction over a period of years; Kafka has become an essential part of designing event stream applications.

Kafka is now being used for capturing data in real-time from event sources like databases, sensors, mobile devices, cloud services, and software applications. This data is captured in the form of streams of events and stored in Kafka for retrieving, manipulating, processing, and reacting to the event streams in real-time. Kafka-based applications ensure a continuous flow and interpretation of data so that the right information is at the right place, at the right time.

Spring Cloud Streams

On the other side, Spring Bot transforms the way we develop production-grade spring-based micro-servers. In the recent past, Spring Boot was put together with the Spring integration to create a new project which is Spring Cloud Streams.

Spring Cloud extends Spring Boot’s capabilities to apply a micro-service architecture pattern for creating event-centric applications.

What if these two master technologies are put together? Spring Cloud Stream joins hands with Kafka Streams DSL, and now the user can use it to create stateless and stateful event stream processing micro-services.

Here we are going to create a simple stream listener. The listener is a Kafka Message consumer who will listen to a Kafka topic, read all incoming messages, and log it. Kafka and Spring are highly configurable systems, so; every application has a configuration file. Here application configurations are defined in a hierarchy.

Spring Cloud Stream Configuration code:

spring:

cloud:

stream:

bindings:

input-channel-1:

destination: users

kafka:

streams:

binder:

applicationId: hellostreams

brokers: localhost: 9092

configuration:

default:

key:

serde: org.apache.kafka.common.serialization.Serdeos$StriingSerde

value:

serde: org.apache.kafka.common.serialization.Serdeos$StriingSerde

 

In this code, there are just two configurations defined. The first one is known as the input-output channel binding, and the second one is the binder. The input-output channel binding defines the lists of source and destination. The binder will define the source and technology. The spring cloud offers a bunch of binder technologies.

The user can use Apache Kafka, RabbitMQ, Amazon Kinesis, Google Pubsub, and Azure events hub, and many more.

Spring Cloud offers two types of Kafka bindings such as:

Apache Kafka Binder: The Apache Kafka binder implements the Kafka client APIs.

Apache Kafka Streams Binder: The Kafka streams binder is explicitly designed for Kafka streams API.

Here is the code to create a Kafka listener service that binds to a Kafka input topic and listens to all the incoming messages.

Method signature topic for input stream builder:

public interface KafkaListenerBinding {

@input(“input-channel-1”)

KStream<String, String> inputStream

}

This method will read from a Kafka topic and return a KStream. The KStream is a Kafka message stream made of stream key and stream value.

Kafka Listener Service

This class will be a service that will trigger the cloud stream framework connected to the Kafka input channel using the Kafka stream API and start consuming the input messages as a KStream.

Here is the code to bind this class with the spring cloud stream infrastructure and pass each message in the KStream.

import Lombok.ecten.log4j;

import org.springframework.cloud.annotation;

import org.springframework.stereotype;

 

(KafkaListnerBinding.)

KafkaListnerService{

(“input-channel-1”)

process(KStream<String, String> input){

input.foreach ((k, v)-> log.info(string.format(“Key: %s, Value: %s”, k, v)));

}

 

}

 

Summary

  1. We started to define some application configurations. In the above configuration code, we configured an input channel with the destination as the user’s Kafka topic because we wanted to connect to the user’s Kafka topic and read all the messages.
  2. Then we told spring cloud stream that we want to use the Kafka Stream binder. So, the spring cloud stream should connect to the Kafka broker using the given hostname and port.
  3. We also configured the message key and the message value type.
  4. Then we define the Listener service. The will trigger the spring framework. The Spring cloud framework will implement the binding interface and create a Kafka stream. The listener method will receive the input stream and send it to the log.

Note: The spring cloud framework must have picked the input channel. And bound to the users’ Kafka topic.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Orchestration with AWS ECS

Orchestration

In the past, data ingestion was done as part of a scheduled batch job overnight, but the cloud has changed because we can no longer assume that our systems will be living adjacent to each other in the data centre. Orchestration is the automated configuration, management, and coordination of computer systems, applications, and services. Moreover, Orchestration helps IT to easily manage complex tasks and workflows.

It also helps you to streamline and optimize frequently occurring processes and workflows, which can support a DevOps approach and help your team deploy applications more quickly.

The user can use orchestration to automate IT processes such as server provisioning, incident management, cloud orchestration, database management, application orchestration, and many other tasks and workflows. Here we are discussing orchestration with AWS ECS.

AWS Serverless

At AWS, we have four criteria such as:

  1. No infrastructure provisioning, no management: There should be no infrastructure that the user needs permission or to manage; no virtual infrastructure in the sense of virtual machines, physical machines, or even any container orchestration.
  2. Automatic-Scaling: This is the pretty core concept with cloud computing; as traffic or requests and events come in, the infrastructure should scale up, and then as they go, they should scale down.
  3. Pay for Value: Pay what you use.
  4. Highly Available and Secure: Every organization considers security their top priority, and AWS also helps their customers build highly available and resilient applications.

Container Orchestration Capabilities

Scalability

Performance, Responsiveness, Efficiency.

Availability

Fault tolerance, robustness, reliability, resilience, and disaster recovery.

Flexibility

Container orchestration provides format support, interoperability, extensibility, and container runtimes.

Usability

Familiarity, maintainability, compatibility, and debug ability.

Portability

Host operating system, cloud, bare-metal, and hybrid.

Security

Encryption quality, vulnerability process, fast patching, and backporting.

Why Need Container Orchestration?

If an organization runs a microservice application, they need containers for each because they scale pretty quickly. The application could be a messaging system or authentication services etc. They need a bunch of containers to deploy their application in some environment. Here AWS virtual service helps as an enterprise application needs ten containers for each of their ten micro-services. Here the question is, how do they manage these containers.

  1. How resources are still available?
  2. Are containers crashed?
  3. Schedule the next container?
  4. Remove multiple replicas?

For the solution, the user needs some automation tool.

Features of Container Orchestration Tools

Container Orchestration tools help to manage, scale, and deployment of containers.

Elastic Container Service (ECS)

ECS being an orchestrator for containers, will manage the whole life cycle of a container. When a container starts, it needs to reschedule the container, restart, load balancing, etc.

How does ECS work?

Moreover, on AWS, if a user wants to create a container cluster managed by AWS ECS service, they need to create an ECS cluster. The ECS cluster contains services to manage containers. So, the ECS cluster represents a control plane for all the virtual machines that are running containers. And the control plane in the services can be managing the whole life cycle of a container from being started scheduled to being removed as the containers need to run somewhere, so the containers need to run on virtual machines. These virtual machines will be the EC2 instances; these instances will host the containers.

Which Services are running on EC2 Instance?

ECS with EC2 Instances

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Drools Rule Engine Works

How Does Drools Rule Engine Works?

The rule engine is a complex event processing system based on rule chains. Here we are going to discuss how Drools Rule Engine works.

Introduction to Drools Rule Engine

Enterprise systems usually have multiple layers. From top to bottom, they are Presentation Layer, Business Logic Layer. The middle layer is a business logic layer representing the core of the application where all of the business processes and decisions occur.

We had a lot of frameworks that covered the User Interface and the Service layer aspects but no proven framework/tool to handle the business logic layer.

Also, the need to build a more complex system is increasing. We are trying to automate all kinds of business processes and implement complex business decisions. However, these processes and decisions are not very well represented using traditional programming languages such as Java; hence needed a framework/tool for the business layer as well and that gave rise to Drools (Rule Engine). Drools is a powerful decision management system with complex event processing features.

Drools is a part of the JBoss Enterprise BRMS product since federating in 2005, is a Business Rule Management System (BRMS) and rules engine written in Java which implements and extends the Rate pattern-matching algorithm.

Rule Engine – Drools

Rule Engine: The rule engine is the computer program that delivers KRR functionality to the developer.

Rule: A rule is a two-part structure.

end

When all the conditions are met, a rule will fire, i.e., actions will execute.

Example

rule “Hello Jhon”

When

User (name == “Jhon”)

then

System.out.println(“Hello Jhon”);

end

Difference Between a Java Method and Rule

Java Method

public void (User user) {

if (user.getName().equalsIgnoreCase(“Jhon”)){

System.out.println(“Hello Jhon”);

}

}

Rule

rule “Hello Jhon”

when

User (name == “Federe”);

then

System.out.println(“Hello Jhon”);

end

Traditional Programming Vs. Declarative Programming

Traditional Approach

Declarative Approach

Advantages and Disadvantages of using Rule Engine

Advantages

Disadvantages

When not to use Rule Engine

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Apache HBase

Why need Apache HBase?

The traditional data storage system is a relational database management system (RDBMS) for storing data and maintaining related problems, but slowly we faced the rise of Big Data. So since the rise of Big Data, we have come across new solutions, and Hadoop is one of them. But when we stored a huge amount of data inside Hadoop and tried to fetch a few records from it, it was a major issue because the user has to scan the entire Hadoop distributed file system to fetch the smallest record. Hence, the limitation of Hadoop is that it does not provide random access to databases. Here this problem can solve using Apache HBase.

What is Apache HBase?

Apache HBase is similar to database management systems, but it also can access data randomly. HBase is a distributed column-oriented database built on top of Hadoop’s file system. It is an open-source non-relational distributed database written in Java. It is developed as a part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS.

Apache HBase is horizontally scalable and similar to Google’s Big Data table design to provide quick random access to huge amounts of structural data. It leverages the fault tolerance provided by the Hadoop file system. It’s part of the Hadoop ecosystem that provides random real-time read and write access to the data in the Hadoop file system.

Apache HBase VS. Hadoop Distributed File System (HDFS)

HBase HDFS
HBase is built on top of HDFS. HDFS is a distributed file system that stores huge files.
HBase provides faster and individual file lookups. HDFS does not support individual file lookups.
It has low latency. It has high latency.
It has in-built Hash tables enabling. Only sequential memory access is available.

 

Apache HBase is a column-oriented database, and the tables in it can be sore by row. The table schema defines only column families, which are the key-value pairs.

Apache HBase Features

Characteristics of HBase

HBase is a type of NoSQL database and is classified as a key-value store in HBase:

Storage Model of HBase

The two major components of the storage model are as follows:

Partitioning:

Persistence and Data Availability:

When to Use HBase?

HBase: Real Life Connect

Facebook’s messenger platform needs to store over 135 trillion messages every month. They store such data in HBase. Facebook chose HBase because it needed a system that could handle two types of data patterns: an ever-growing data set that is rarely accessed and an ever-growing data set that is highly volatile.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC

Databricks and Apache Spark

What Databricks Offers Beyond Apache Spark?

Apache Spark

Apache Spark is an open-source project data management tool specifically for cluster computing with other features that come out of the box and make it a good data analytics engine. Specifically, Apache Spark descended from previous technologies such as Hadoop and MapReduce. It does many intermediate operations entirely in memory without writing their results back on disk, increasing the total speed of users’ processing.  Besides mapping and reducing, it also processes SQL queries, streaming data, ML models, and graph calculation.

Databricks

Databricks, as a comparison, is also a data analytics server, but this one is a managed service in the cloud. It is not only managed by the data analytics service; there are others like Stratio, but Databricks is famous specifically because its developers are also some of the original developers of Apache Spark. Databricks contains a modified Spark instance called Databricks runtime, which has some improvements and optimizations over base Spark both for normal processing and connections to the things. It connects to several external technologies and many internal tools, and it’s cloud-native on Azure and AWS.

Features Comparison Databricks and Apache Spark

Here we discuss a feature comparison of Databricks and Apache Spark and the difference between Databricks and Apache Spark.

Databricks Runtime

Notebooks

Machine Learning Frameworks

MLflow / AutoML Frameworks

BI Tool Integrations

Delta Lake/Data Lakes

Apache Spark

 

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon Zookeeper

What is Apache Zookeeper System Design?

Apache Zookeeper

Apache Zookeeper is a pillar for so many distributed applications because of its unique features.  It uses as coordination between distributed applications. It exposes a simple set of primitives to implement higher-level services for synchronization, configuration, maintenance, groups, and naming. Zookeeper’s design is easy to use and program. It is run on java and has bindings for Java, Python, and C language.

Apache Zookeeper also provides service for distributed open-source centralizes, and coordination:

Companies Using Zookeeper System Design

Yahoo

Why need Apache Zookeeper System Design

“Primitive” Operations in a Distributed System

Apache Zookeeper

Sequential Consistency

Atomicity

Single System Image

Durability

Once an update has succeeded, it will persist and will not be undone.

Timeliness

Rather than allow a client to see very stale data, a server will shut down.

Features

Apache Zookeeper also has the following characteristics:

Apache Zookeeper Design Goals

Simple

Apache Zookeeper is Replicated

Ordered

The number:

Zookeeper is Fast

Multiple Updates

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

AI help with Product Recommendation

Why Should Businesses Use AI for Product Recommendation?

AI-based recommendation systems are great applications for internet-based companies, especially E-commerce and streaming businesses. The significant benefit for e-commerce companies is that all the necessary user data can be captured when users visit an e-commerce business website. AI helps to get powerful analytics that can be useful to grow the business.

1.    Improve Customer Satisfaction

The key benefit of using AI is to improve customer satisfaction as the system can provide the user with more meaningful content, whether these are products, songs, or videos.

2.    Provide Personalization

The personalization levels increase massively because an e-commerce website built a customer profile. The website owner gets the opportunity to learn from their customer’s data and provide them with a personalized user experience.

3.    Improve Product Discovery

Because of personalization, product discovery will improve as users might be finding products they usually wouldn’t.

Recommendation Engines

The way recommendation engines work is as follows three main types such as:

1.    Collaborative Filtering

It can predict the behaviour of users based on the similarity they have with other users. In the example of Netflix, the system can recommend movies without understanding what the movie is about.

Collaborative Filtering Using Machine Learning Cycle

The machine learning cycle for collaborative filtering consists of the following steps such as:

Data Source: Understand the data source and business with all machine learning projects. In this case, the generic user behaviour serves all users and the activities and preferences they have.

Data Preparation: In the second step, the user has to select the data, clean it, and transform it into the algorithm.

Algorithm Application: There are two key algorithms for collaborative filtering:

user-user collaborative filtering searching for lookalike customers and offering products based on what the lookalike has chosen. And the second main algorithm is item-item collaborative filtering, in which, rather than finding a lookalike customer, it finds products that are lookalike.

Algorithm Optimization: In the fourth stage, we will compare the algorithms, the impact, and the results are measured, and we can do that by measuring the increased revenue or the increased watching time. This cycle is then repeated until the results are to an acceptable standard. As already mentioned earlier, the coded-based filtering approach is based on item features and user profile data. This data can range from age, demographic power, sales history, click rates, etc. And the item features can be based on specified contents or labels of the item, for example, natural language processing.

We used to understand the underlying content, and once the raw data is prepared, we can apply many different algorithms to this application. One is cluster analysis which groups data objects based on information that describes the object and their relationships. The second is the neural network that can train to protect ratings or interactions based on the item and user attributes.

The user can also use deep neural nets to predict the next action based on historical actions and contents.

2.    Content-Based Filtering

This is a more complex approach that consists of two main factors, the user profile and the items such as a product. It tries to recommend products which have similar to the ones that a user has liked in the past; the way it worked is that the system builds a profile of the user based on, for example, their search history click behaviour interests and tries to find similar features of an item such as a movie or a product. This requires a system to understand the content of the item. For example, when a user watches an action movie with certain actors’ ratings and other features, the system can recommend movies that have similar content that those the user has already watched; the hybrid recommendation system is a combination of the first two where it combines the outcomes of each and puts them together using certain scoring criteria.

3.    Hybrid Recommendations

Hybrid Recommendations

The hybrid engine combines the input from both mentioned systems to provide recommendations; these are often complex mathematical calculations that take various criteria from each engine to combine to achieve the highest quality recommendation engine.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon Neptune

Highly connected data is essential for many of today’s applications, including knowledge graphs, identity graphs, fraud graphs, social networking, and recommendation engines. Corresponding data needs to be managed and queried in a simple and fast way. But traditional databases are too rigid, and existing graph databases are difficult to scale as applications grow. Here we are discussing Amazon Neptune – Graph Database.

Amazon Neptune

Amazon Neptune is a fast, reliable, fully-managed graph database service that helps to build and run applications that work with highly connected data sets. The core of Amazon Neptune is a purpose-built graph database engine.

It is optimized for storing billions of relationships and querying the graph with millisecond latency. It supports popular graph models, property graph, W3C RDF, and their respective query languages Apache, tinkerpop, and sparkle.  So, it’s easy to build queries that are efficiently navigating, highly connected data sets.

Neptune ML Capability

It also uses the amazon Neptune ML capability to utilize graph neural networks. A machine learning technique purpose-built to make easy and fast predictions using graph data. Neptune ML improves the accuracy of most predictions by over 50%; when compared to non-graph methods.

Low Latency: Amazon Neptune supports low latency read replicas across three availability zones.

Scalability: The user can easily scale their database deployment up and down as their needs change.

Availability & Durability: It is highly available, durable, and compliant, designed to provide greater than 99.99 percent availability. It features fault-tolerant and self-healing storage created for the cloud, which creates up to six copies of data in three different accessibility zones. In addition, it constantly backs up data to the Amazon S3 service and transparently recovers lost data during a disaster.

Security: It allows for multi-level data protection and access to them; with the help of network isolation and virtual private cloud network (VPC) and the possibility of encryption data in rest (using the AWS KMS service).

Pay-Per-Use: Amazon Neptune is a service billed in the pay-per-use model, i.e., payments only for the resources used. This allows its users to free themself from the unnecessary startup costs and complexity of planning the purchase of database capacity in advance.

Performance: Applications can scale out read traffic across up to 15 read replicas.

 

Where to use Amazon Neptune?

An organization can use the Amazon Neptune database in applications made for:

Social Networking

Amazon Neptune also allows its users to process large interaction sets to create social applications quickly and easily. Its functionalities also help to prioritize the order of updates displayed to users.

Supports for Open Graph APIs

Amazon Neptune supports tools such as Gremlin or SPARQL while allowing the selection of the chart model. Properties and language of open-source queries while ensuring high efficiency of their operation.

Recommendation Engines

Amazon Neptune also allows users to use the highly available database more efficiently to create product recommendations. Recommendations can be based on a comparison of such as similar shopping histories among users or mutual friends.

Knowledge Graph

Education is another area where an organization can apply this database model. Using knowledge charts, the user can easily update information or expand and check complex regulatory rules models. An example is the Wikidata portal.

Life Science

With the Amazon Neptune database, users can store data such as disease models and genetic patterns. It helps to easily model relationships and chemical reactions that can be used in scientific publications.

Network and IT Operations

Moreover, it gives the ability t store and process events to manage and secure the network. Using this service, the user can easily understand how an anomaly can affect the network.

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Kubernetes Vs. Docker Swarm

Kubernetes Vs. Docker Swarm

What is Docker?

Docker is a platform used to containerize software. The user can easily build their application and package them with the dependencies required for their application into the container. Further, these containers are easily shipped to run on other machines. So, Docker simplifies the DevOps methodology by allowing developers to create templates called images using which the user can create these lightweight virtual machines called containers. Docker makes things easier for software industries, giving them the capability to automate the infrastructure, isolate the applications, maintain consistency, and improve resource utilization.

What is Kubernetes? Kubernetes is a container management system developed in the Google platform. It helps users manage a containerized application in various physical, virtual, and cloud environments. So, Google Kubernetes is a highly flexible container tool to deliver even complex applications consistently. It can run applications on clusters of hundreds to thousands of individual servers. Here we are discussing Kubernetes Vs. Docker Swarm.

Kubernetes Vs. Docker Swarm Features

Features of Docker

Easy Configuration

Easy configuration is one of the core features of Docker because it allows users to deploy a code in less time and effort as it provides a wide variety of environments.

Use Swarm Easily

It is a clustering and scheduling tool for Docker containers whose swarm uses the docker API as its front, which helps us to use various tools to control it. Swarm also allows us to control the cluster of the docker host as a single virtual host. It is a self-organizing group of engines that is used to enable pluggable backend.

Security Management

Docker allows us to save confidential data into the swarm itself and then choose to give services access to certain secrets. It includes some essential commands to the engine like secret inspection etc.

Services

It also provides a list of tasks that allows its users to specify the state of the container inside a cluster, and each task represents one instance of a container that should be running, and the swarm should use them across the nodes.

Productivity

Increased productivity by easing technical configuration and rapid deployment of an application. Docker not only helps to execute the application in an isolated environment but also has reduced the resources.

Application Isolation

It provides containers used to run applications in an isolated environment so, each container is independent of another and allows us to execute any application.

Features of Kubernetes

Runs Everywhere

Kubernetes is an open-source tool and provides freedom to take advantage of on-premises hybrid or public cloud infrastructure, letting to move workloads anywhere.

Automation

It automates various manual processes; for instance, Kubernetes will control which server will launch the container and how it will be launched.

Interaction    

It interacts with several groups of containers. Kubernetes can manage more clusters at the same time.

Additional Services

Kubernetes provides additional features as well as the management of containers.

Security and Storage Services

Kubernetes also offers security networking and storage services.

Self-Monitoring

It also provides the provision of self-monitoring as it constantly checks the health of the system and containers themselves.

Horizontal Scaling

Kubernetes allows scaling resources not only vertically but also horizontally.

Kubernetes Vs. Docker Swarm

Kubernetes Docker Swarm
Created by Google; now maintain by CNCF It is created and maintained by Docker Inc.
Backed by a huge developer community Developer community not as big as Kubernetes
Preferable for complex architecture Preferred for simple architecture
Better when 100s-1000s containers are in use Better when 10-20 containers are in use
Setting up the cluster is challenging and complicated Setting up the cluster is simple requires only two commands
Cluster strength is strong Cluster strength is not as strong
Provides an easy to use GUI so apps can be easily scaled and deploy There is no GUI available
Scaling is easy Scaling up is 5x faster than Kubernetes
Based on server traffic, containers will be scaled automatically by Kubernetes Scaling up or scaling down has to be done manually

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

,Elastic Cloud on Kubernetes

Kubernetes is a portable, extensible, and open-source platform for managing containerized workloads and services that facilitate declarative configuration and automation. It allows us to run distributed systems resiliently, with scaling data and failover for applications. It provides Self-Healing for crash data, Automatic Binpaching, Service discovery, load balancing, storage orchestration, secret and configuration management, Batch execution, horizontal scaling, and automatic rollbacks and rollouts.

Here we are discussing Elastic Cloud on Kubernetes.

Container Orchestrator

It is a container orchestrator that helps its users to make sure that each container is supposed to be active and that the containers can work together.

Challenges of Running on Kubernetes

There is a certain number of resources a user needs to manage on their Elastic Cloud on Kubernetes.

Managing Resources

Operations

Stateful Workloads

Why Kubernetes

Scalability

It also provides a micro-service model with scaling benefits for databases to manage and transform data and memory space more accurately without wasting space idle. The individual services can scale to match their traffic without over-provisioning.

Containerized Applications 

Having one machine for each service would require many resources and a whole bunch of machines and be costly. That’s why Kubernetes’s containers are a perfect choice. Containers allow teams to package up their services accurately with all the applications, and their dependencies, and any necessary configuration gets delivered together.

How does Kubernetes help with Container Upgrading?

Upgrading a container is also easy since the user can create a new version of the container and deploy it in place of the old one. But how can upgrades be done without downtime is a problem? And how can an application developer debug the issue and observe what’s happening?

Here Kubernetes API is all about managing the containers on virtual machines or nodes. The nodes in the containers are run grouped as a cluster, and each container has an endpoint, DNS, Storage, and scalability. The Kubernetes automates most of the repetition and inefficiencies of doing everything by hand.

Elasticsearch Cloud on Kubernetes (ECK)

Elastic Cloud on Kubernetes automates the deployment, provisioning, management, and orchestration of Elasticsearch, Kibana, APM Server, Enterprise Search, and Beats on Kubernetes based on the operator pattern. ECK is a Kubernetes operator; the operators are the clients of the Kubernetes API; they will communicate with Kubernetes API.

The ECK allows us to deploy the entire Elastic Stack, Elasticsearch, Kibana, APM Servers, Enterprise Search, and Beats on the Kubernetes cluster. ECK is also compatible with most Kubernetes distributions, including Openshift, Vanilla, etc. It is built on the Kubernetes Operator pattern and extends Kubernetes orchestration capabilities to support the setup and management of Elasticsearch and Kibana on Kubernetes.

Moreover, it supports orchestrating Elasticsearch with advanced topologies such as Hot / Warm / Clod nodes deployment. The Hot nodes would have high-speed underlying storage.

Deploy and Manage

Elasticsearch, Kibana, and APM Server, and more.

Supports Multiple Kubernetes Distros

Azure Kubernetes Service (AKS), Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), Vanilla Kubernetes, and Red Hat Openshift.

Multi-Cluster Management

It allows us to deploy one or dozens of clusters.

Automatic Security

All clusters have security and TLS configured.

Smooth Operations

Scale-up, scale down, rolling upgrades with no downtime nor data loss.

Advanced Topology

Hot-warm-cold deployments, dedicated masters / ingest, and machine learning.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Log Analytics with ELK Stack

What is Log Analytics?

Log analytics is the science of analyzing raw data to make conclusions about that information. This information will be helpful to optimize processes to increase the overall efficiency of a business or system. When an analyst tries to find out an error or try to find out on which server it is actually running and then evaluate the logs, this will be a very time-consuming and tedious process. Here we are going to discuss how to visualize or Log data using the various visualization solutions. Log analysis allows us to place large logs in a central place and analyze it. Log analysis can be centralized or decentralized.

What is ELK Stack?

ELK Stack is a combination of three open-source tools which form a log management tool/platform that helps in deep searching, analyzing, and visualizing the log generated from different machines. It is a combination of Elasticsearch, Logstash, and Kibana. Each of these components has its role to play.

Elasticsearch Features

Elasticsearch is a tool that plays a major role in storing the log in the JSON format, indexing it, and allowing the searching of the logs. Elasticsearch is a tool that generally works on the data which has been collected. The data collected is being converted into or indexed to retrieve useful information when required.

Why use Elasticsearch

Elasticsearch provides approximation methods to provide relevant results for count distinct or percentage queries. It also has some support for streaming ingestion.

Logstash Features

Logstash is an open-source tool used to collect, parse and filter Syslog as the input. Whatever data is coming from the servers, it is centrally taken, or it is centrally pulled by the Logstash tool into a central place, and it is further kept at a place where the Elasticsearch works upon that data. So, its primary role is to collect, parse, and Syslog data as the input.

So, it works as a pipeline where from one end, the data is input from the servers, which are there in the server form; from the other end, the Elasticsearch takes out the data and converts it into useful information. It centralized the data processing, collects, parses, analyzed the structured and unstructured data. Some of the features of Logstash are as follows:

Kibana Features

Kibana is a web interface which is allowing us to search, display and compile data. It is responsible for presenting the data in the visual format in the user interface. It shows reports in the form of charts, bar graphs, and other graphical representations. It is also very capable of providing any information in the form of a report. Its expertise can extend by using different plugins.

Companies Using ELK Stack

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Product Owner Roles and Responsibilities

Who is the Product Owner?

The primary intent of a product owner is to represent the customer to the development team as a key responsibility. The key responsibility of this job profile is to manage and give visibility to the product backlog or the prioritized list of requirements for future product development.

The Product Owner

He is the only person that has the authority to change the priority requirements in the product backlog. Here we are going to discuss the product owner’s roles & responsibilities.

Product backlog management includes the following things:

The role of a product owner includes:

Somebody that does all of the above tasks is known as a product owner.

Product Owner vs. Product Manager

A product manager and a product owner revolve around their mindset when approaching a problem to be solved.

Moreover, they focus on the internal issues that require implementation inside the company and the development process. The product manager has an external focus; the product manager will mostly talk about long-term strategy, markets, and customer needs.

 Roles and Responsibilities

How to be an Outstanding Product Owner?

A (PO) plays a significant role in the success of a product.

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Amazon Kinesis

Amazon Kinesis makes it easy to collect, process, and analyze real-time streaming data. So the user can get timely insights and react quickly to new information. It is also known as a streaming pipeline that allows its users to get data into the Elasticsearch service.

Three sub-services come with different capabilities under the Amazon Kinesis service group.

Kinesis Streams: Amazon Kinesis Streams stores data as a continuous replayable stream for custom applications. The user can use different frameworks or technologies according to their choice to process the data in real-time, panicking the stream. A KCL at Kinesis’s Client Library application can use spark streaming. The user can use the Lambda function or Kinesis analytics.

Kinesis Firehose:  It’s an abstraction layer on top of the Kinesis stream. It automatically loads streaming data in real-time into different analytical and data storage destinations, including the S3 Redshift and Amazon Elasticsearch service.

Kinesis Analytics: Kinesis Analytics allows its users to use standard language to run queries and analyses against the data stream directory. So, the user can use the SQL scale, which most of customers already have today, to run a real-time analysis against the real-time data stream to get analysis results.

What is Kinesis Firehose?

It allows its users to deliver streaming (event) data into destinations such as BI databases, data exploration tools, dashboards, etc. It’s fully managed with elastic scaling that responds to increased throughput and allows users to batch many events into a single output file.

Key Concepts of Amazon Kinesis Firehose

Delivery Stream: The underlying entity of Firehose. The user can use Firehose by creating a delivery stream to a specified destination and sending data to it.

Record: The data of interest that an organization’s data producers send to a delivery stream. A record can be as significant as 1000 KB.

Data Producers: Producers send records to a delivery stream. For example, a web server that sends log data to a delivery stream is a data producer.

Data Flow Overview of Amazon Kinesis

  1. Capture data and submit streaming data to Firehose.
  2. Firehose loads streaming data continuously into Amazon S3, Redshift, or Elasticsearch Service.
  3. Analyze streaming data using any analytical tool.

Zero Administration: Capture and stream data into Amazon S3, Redshift, and Elasticsearch Service without writing an application or managing infrastructure.

Direct-to-Store Integration: Batch, compress, and encrypt streaming data for delivery into data destinations in as little as 60 seconds using simple configurations.

Seamless Elasticity: Seamlessly scale to match data throughput without intervention.

 

Amazon Kinesis – Firehose Vs. Stream

Amazon Kinesis Stream: It’s for the use case that requires custom processing, per incoming record, with sub-second processing latency and choice of stream processing frameworks.

Amazon Kinesis Firehose: Kinesis Firehose is for use cases that require zero administration, the ability to use existing analytics tools based on Amazon S3, Amazon Redshift, Amazon Elasticsearch, and a data latency of 60 seconds or higher.

Why Kinesis Firehose for Elasticsearch

Amazon Elasticsearch service is a cost-effective managed service that makes it easy to deploy, manage, and scale open-source Elasticsearch for log analytics, full-text search, and more. An enterprise can run all its streaming applications without having to deploy and maintain costly infrastructures. Amazon Kinesis can handle any amount of streaming data and process it from hundreds of sources with low latency.

Amazon Elasticsearch Service Benefits

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Identity and Access Management

Amazon Identity Management (IAM) allows its users to manage access to compute, storage, database, and application services in the AWS cloud. IAM uses access control concepts, basic concepts such as users, groups, and permissions, which get applied to individual API calls. So, it allows us to set permissions to control users can access to services, user’s actions to perform with a service, and which resources are available, ranging from virtual machines, database instances, and even the ability to filter database query results.

What is Identity Access Management (IAM)?

AWS Identity Access Management (IAM) is a web service that helps its users securely control access to AWS resources for an organization’s users. IAM also allows to control who can use AWS resources (authentication) and what resources they can use, and in what ways (authorization).

Components

Users

Using IAM allows the creation and management of AWS users and uses permissions to allow and deny their access to AWS resources.

Groups

It also allows us to create groups by creating more users, and then the rules and policies that apply to the group apply on the user level as well.

Roles

An IAM role is an IAM entity that defines a set of permissions for making AWS service requests. IAM roles are not associated with a specific user or group trusted entities assume roles, such as IAM users, applications, or AWS services such as EC2.

Policies

To assign permissions to a user, group, role, or resource, the user needs to create a policy, which is a document that explicitly lists permissions.

Multi-Factor Authentication

IAM provides something like OTP that the user will get when they log into their Gmail account. Multi-factor authentication is two layers of security. One layer is a password and the second layer becomes the verification code that we’ll be entering.

With AWS, the Google Authenticator application allows us to create a virtual multi-factor authentication device to create in AWS.

Security

Security is very important for Amazon web services customers. In addition to physical security to provide fine-grained access and data, locality controls Amazon web service provides the infrastructure building blocks to build sophisticated secure applications, which meet the regulatory and compliance standards.

Focus on Features and Functionality

Identity Access Management lets developers focus on the features and functionality of their application software while it does the heavy lifting on the security side of things.

For instance, IAM can automatically rotate access keys on virtual machine instances, ensuring that only trusted applications and users have appropriate access at any given time. There is no additional charge for IAM, and getting started is easy.

How does IAM Work?

Principle

Request

Authorization

Actions

Resources

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Splunk vs. ELK

Here we will discuss and compare the benefits of “Splunk Vs. ELK,” including their integration. Here we analyze the efficiency of Splunk and ELK for all sizes of businesses.

Splunk Vs. ELK

ELK

Splunk Enterprise

Splunk also captures indexes and correlates real-time data in a searchable repository from which the user can generate graphs, reports alerts, dashboards, and other visualizations. It helps to produce valuable business insights among the machine data types. Splunk can analyze application logs file system, Audit logs, SCADA data, and web access logs. It uses Search Processing Language (SPL); it’s a decomposed JSON format.

 

Category Splunk ELK Stack
Features Splunk has a search capability, Reporting, Alerts, and data visualization. Search capability, Reporting, Alerts, and data visualization.
Setup and Maintenance Easy Bit challenging
Solution On-Perm and SaaS On-Perm and SaaS
API & Extensibility Two hundred plus API Provides API support
Plugin Support Yes Yes
Components Forwarder, indexer, and search head Logstash, Elasticsearch, and Kibana
Search SPL Query DSL
Compression Yes No
Customer Support Proficient Good
Community Support Good Community Support Better than Splunk

 

Splunk Vs. ELK

ELK Splunk
ELK is an open-source tool. Splunk is a commercial tool.
ELK stack does not offer Solaris portability because of Kibana. Splunk offers Solaris portability
Processing speed is strictly limited Offers accurate and speedy processes
ELK technology stack created with the combination of ElasticSearch-Logstash-Kibana Splunk is a proprietary tool. It provides both on-premise and cloud solutions.
In ELK, searching analysis and visualization will only be possible after the ELK stack setup. Splunk is a complete data managing package at user’s disposal.
ELK is not supporting integration with other tools. Splunk is a useful tool for setting up integration with other tools.

Splunk Pros & Cons

Pros

Cons

Splunk provides a clean, intuitive user interface. Splunk can be expensive
The user can connect Splunk to almost any machine data source. Requires learning SPL

 

Flexibility and the ability to conduct fast searches over large data volumes. Does not support no-code experience
Easy to deploy and provides highly customizable solutions for enterprises that require fast search over large data volumes. Time taking integration
Splunk is on a security analytics mission; most enterprises use Splunk in some capacity for infrastructure monitoring application analytics or security. For security, Splunk is building its future around its cloud-based unified security platform. Splunk has been slower to the cloud than others in this evaluation and cloud-native newcomers to the security analytics market.

 

ELK Stack Pros & Cons

Pros

Cons

ELK stack offers incredible scalability with a massively distributed structure. Tuning for ingress performance can be tricky.
Elasticsearch clusters can detect failed nodes to organize and distribute data automatically. The documentation could be a bit more detailed and have more examples, especially for advanced functionality.
Elastic stack offers full-text searching capabilities with a query API that supports multilingual search geolocation, contextual suggestions, auto-complete, and result snippets. The ingest pipeline structure is more complicated and confusing than previous implementations for using things like attachment plugins.
It has a very powerful aggregation engine that can allow for tons of customizable analytics and reports. Complex query mechanism and architecture to set up and optimize.
Elasticsearch has a new Elastic Cloud SaaS solution which is very easy to deploy, set up, and scale with all features and more. The user interface is heavy in Java requirements, and sometimes the user can get some lag displaying heavy results for heavy queries.

 

Author: SVCIT Editorial Copyright
Silicon Valley Cloud IT, LLC.

Software as a Service (SaaS)

The Software as a Service (SaaS) provider manages everything from hardware installation, working to app functioning.  End users are not responsible for anything in this model; they only use programs to complete their tasks. SaaS is a part of almost everyone’s daily life. Software as a service or SaaS is one of the three main categories of cloud computing and the most common with consumer-level products alongside Infrastructure as a Service and platform as a service. Simply put, the “as-a-service” translates to over the internet.

Software as a Service (SaaS) Applications

So SaaS is a third-party application available over the internet, with no physical connection to anyone’s device. The email clients are likely SaaS, Google Docs, Salesforce, Cisco WebEx, Slack, and Microsoft office 365 are SaaS, providing productivity apps over the internet. For businesses, there is SaaS for sales management, customer relationship management, financial management, Human resource management, billing, collaboration, etc.

Software as a Service (SaaS)  applications are used by a range of IT professionals and business users, and C-level executives. Leading SaaS providers include Salesforce, Oracle, SAP, Intuit, and Microsoft.

Popular SaaS Providers

The Google ecosystem such as:

 

Cost-Effective  

Because Software as a Service (SaaS)  eliminates the expense of hardware, maintenance, licensing, and installation, it can be cost-effective. SaaS offerings generally operate on a pay-as-you-go model, offering businesses flexibility.

Advantages

Disadvantages

Wide Variety of SaaS Applications

SaaS Is a Focal Point of Enterprise Digital Transformation Strategies

The Principles of SaaS Operations

Cost Management

With the growing usage of Software as a Service (SaaS)  technology by the enterprise, the licenses are neither overprovisioned nor under-provisioned. Identify unused licenses for repurposing is a big opportunity in this environment.

Security & Access Management

Many global enterprises have or are implementing their single sign-On portals to employees for SaaS-based app access.

Classic Operations

Enterprise needs to understand a consolidated workflow management solution across multiple APIs to help with the day-to-day administrative tasks associated with SaaS applications.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

AWS IoT Greengrass Technology

Building device software can be a long process. Customers often find themselves reinventing the wheel device builders want to easily build, deploy and manage device software for use in homes, factories, vehicles, and businesses. This requires developing and debugging applications on a variety of test devices and deploying software to millions of devices globally.

AWS IoT Greengrass technology helps us accelerate time to market and reduce costs in two ways; first, it lets its users quickly build intelligent device software with a robust edge runtime. Second, it lets its users remotely deploy and manage device software.

 

AWS IoT Greengrass

AWS IoT Greengrass brings cloud programming and functionality to two sets of IoT devices, empowering them to communicate and react when a cloud connection is impossible. These devices together are known as the Greengrass group. The groups are always defining and configure from the cloud. In this case, the group is defined around a manufacturing site. The first step in creating a new group is to establish a Greengrass core in this cloud definition.

AWS Greengrass Group

An AWS Greengrass group is a set of cores, and it can configure other devices to communicate with one another.

Every group needs the Greengrass core to function properly. Adding a core to the cloud definition of a group represents a physical device on which the user can install Greengrass core software that core software securely connects the device to AWS installed by the user on their core device. The AWS IoT Greengrass user can also define their group in the cloud; the user can add other provisioned AWS IoT devices to their group definition or AWS lambda functions.

Simple programs that can process or respond to data. The user can build and edit their definitions safely in the cloud and then deploy their group to make it functional once the user deploys their groups’ devices and programs can communicate and react even without the connection to the cloud.

AWS IoT Greengrass enables local processing, messaging data management, and ML inference and offers pre-built components and building blocks to help in the development of edge applications.

Security

AWS IoT Greengrass provides a secure way to seamlessly connect the edge devices to any AWS service such as Amazon Kinesis, CloudWatch, or S3 and third-party services.

Once software development is complete, the user can deploy and manage their software on millions of devices without needing a firmware update.

IoT Devices

Greengrass works with IoT to maintain long-lived connections and process data via the rules engine. The IoT devices have a long life span, and updating software remotely is critical in keeping devices up to date and making them smarter over time. The user can install the AWS IoT Greengrass client software on IoT devices or hubs. Hubs allow other edge devices to communicate with each other even without a cloud connection.

AWS IoT Greengrass helps build and manage device software, allowing users to invest more time and energy in their core value proposition.

Greengrass Components

AWS Greengrass Core (GGC)

The runtime is responsible for Lambda execution, messaging, device shadows, security, and interacting directly with the cloud.

IoT Device SDK

Any device that uses the IoT device SDK can be configured to interact with AWS Greengrass Core via the local network.

AWS Greengrass Capabilities

 

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Today the decentralization with over 130 thousand active editors maintains a different type of record in the form of pages. The risk of security is much smaller since each edit is public and can be verified by anyone. Here the decentralization technique helps to reduce the risk for corruption, fraud detection, and manipulation. Here we are going to discuss what blockchain is & how it works.

Blockchain Technology is the latest and innovative way to implement decentralization.

Blockchain Technology

It is a solution to the problem of decentralization. Blockchain Technology

is a system for keeping records by everything, without any need for a central authority. A decentralized way of maintaining a large amount of data that is practically impossible to falsify, which means when so many eyes are watching and verifying everything that’s being done, it’s hard to break the rules unnoticed.

Blockchain technology is a distributed data record that is completely open to anyone. Blockchain Technology’s interesting property is once some data has been recorded inside a blockchain, it becomes very difficult to change it.

Why Blockchain Technology?

Suppose we store records on pages with many pages, and each page begins with a sort of summary of the page before it. If we change a part of the previous page, we also have to change the summary on the current page. So, the pages are linked or chained together. In technological terms, pages are called blocks. Since each block is linked to the previous block’s data, we have a chain of blocks or a blockchain.

 

Elements of Blockchain Technology

There are four elements of Blockchain such as:

Peer-to-Peer Network

The first thing required to support a blockchain is a peer-to-peer network. A network of computers, also known as equally privileged nodes, is open to anyone or everyone. It’s basically what we already have today with the internet. We need a network so that we will be able to communicate and share remotely.

Cryptocurrency

The second ingredient is cryptography. Cryptography is the art of secure communication in a hostile environment. It allows its users to verify messages, and it proves the authenticity of user’s messages, even when malicious players are around.

Consensus Algorithm

The algorithm for bitcoin is the “Proof of Work” algorithm. This algorithm states that for someone to earn the right to add a new page to the user record, they need to find a solution to a math problem that requires computational power to solve security issues.

Punishment & Reward

This element drives by game theory, and it makes sure that it will be in people’s best interest always to follow the rules. Blockchain sets up a network that has a way to communicate securely and follows a set of rules for reaching consensus.

In Blockchain, use these elements together by rewarding people that help us maintain our records and add new pages. This reward is a token or coin that is awarded each time a consensus has been reached, and a new block is added to the chain.

Blockchain

How does Blockchain work?

Each block contains some data, the hash of the block, and the previous block’s hash. The data that stores in the block depend on the type of Blockchain. The Bitcoin Blockchain, for example, stores the details about a transaction here, such as the sender, receiver, and amount of coins.

A block also has a hash. The user can compare a hash to a fingerprint. It identifies a block, all of its contents, and it’s always unique, just as a fingerprint. Once a block is created, its hash is being calculated. Changing something inside the block will cause the hash to change.

So, in other words, hashes are very useful when the user wants to detect changes to blocks. If the fingerprint of a block changes, it no longer is the same block. The third element inside each block is the hash of the previous block. This effectively creates a chain of blocks, and it’s this technique that makes a blockchain secure.

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Introduction Amazon Aurora

Amazon Aurora

Amazon Aurora is a proprietary technology from AWS (not an open-source), but it’s compatible with PostgreSQL and MySQL, supported as Aurora DB.  Aurora is “AWS Cloud Optimized” and claims 5x performance improvement over MySQL on RDS, over 3x the performance of PostgreSQL. Its storage automatically grows in increments of 10GB; when the user puts more data into their database, it grows automatically up to 64 TB.

Amazon Aurora Benefits

High Performance and Scalability

High Availability and Durability

Highly Secure

MySQL and PostgreSQL Compatible

Fully-Managed

Global Database     

Global Database Features

Sub-Second Data Access in Any Region

Cross-Region Disaster Recovery

Author: SVCIT Editorial Copyright

Silicon Valley Cloud IT, LLC.

Elasticsearch Vs. Algolia and Appbase.io

Elasticsearch Vs. Algolia Vs. Appbase.io

Elasticsearch is open-source analytics and full-text search engine. It helps enable search functionality for applications that can be blog posts, products, or categories.

Algolia is a search provider that allows index data, search filters, and a full-text search engine.

The appbase.io provides a declarative API for creating relevant search experiences. It also provides a control plane that leverages this API as well as a set of UI components.

Here we are going to discuss Elasticsearch Vs. Algolia and Appbase.io.

Implementation

Elasticsearch can be challenging and works best when implemented by a team with some working knowledge and experience. On the other hand, Algolia allows its users to index data from JSON or CSV files straight from their dashboard. The users can also use their APIs to add or update records.

Speed

A common hurdle for Elasticsearch developers is matching the speed of delivery.

Algolia is a hosted search technology that has a fast search speed. Although, Elasticsearch requires significant engineering.

Search Relevance

Elasticsearch uses Leucine under the hood to deliver results and includes typo tolerance synonyms and highlighting; the challenges that developers will face in configuring and iterating on the search relevance. In the case of Algolia, businesses have the controls to configure search-relevant settings from their dashboard and also can go live in real-time.

Search Analytics

Search analytics also provides vital information to businesses. Elasticsearch does not provide out-of-the-box support for search analytics. The user has to implement this independently by instrumenting their code to record telemetry and then create visualizations using a business intelligence tool like Kibana.

Algolia has out-of-the-box analytics functionalities that let businesses monitor search terms, volume, no-result searches, and click analytics.

Search UI Designing

Design search experience depending on the technical expertise and use case creating a search UI for Elasticsearch can take up to a month for some businesses. The user will be responsible for creating the database, index data, write queries, build the front-end UI, and get the entire project production-ready. In the case of Algolia, they create UI libraries that do the job of implementing search much faster.

Total Cost of Ownership

Building in-house search with Elasticsearch has a high total cost of ownership (TCO), but it also fulfills businesses’ need to allocate training, development, and maintain the search. The most significant benefit is that the user can optimize their search to work alongside the rest of their technology stack.

On the surface, Algolia would seem to have a lower TCO but introduce a lack of adaptability and lock-ins to hamper some businesses. The subscription fee can also be high for businesses that have an extensive catalog size. Algolia also does not have the same level of API tooling or ecosystem as Elasticsearch. It is great at search but not at aggregations and providing some search limitations such as fewer languages and a lack of support for certain data types.

Appbase.io

Hybrid Solution

A hybrid solution like appbased.io comes in handy. Businesses can also leverage the flexibility and relevance offered by Elasticsearch along with the out-of-the-box features from Algolia to build search relevance, visualize analytics, and design UI. Moreover, appbased.io enables businesses and developers to build fast and relevant search experiences by using No-Code editor, javascript UI components, or declarative rest APIs.

Search Relevance Control Plane

Search relevance settings like weights, typo tolerance, and synonyms can be set from a point and click control plane in real life.

Query Rules

Configure query rules to extend search relevance by promoting or hiding specific results, changing search behavior, and add facets based on a query catalog or time frame.

Visualize the impact of search with popular search terms, conversions, and the telemetry to record end-user behavior is pre-configured out-of-the-box.

Appbase.io Pricing Plans

The appbase.io pricing plans are based on storage in GB’s and not by total records at scale user can save up to 10x with appbase.io compared to Algolia.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon Elasticsearch Service & APIs

In the field of data science or Big Data, we heard a buzz about Elasticsearch. It allows its users to extract meaning from data at scale. Elasticsearch provides search query results back in milliseconds when other systems like Hadoop or apache-spark might take hours. Elastic search is a scalable version of the Lucene open-source search framework.

Elasticsearch is a potent tool, it’s not just for search. The low-level Elasticsearch is just about handling JSON requests. It’s using a powerful server that can process JSON requests and provide JSON data as a result. Here we are going to discuss how Amazon Elasticsearch and its APIs work.

Service Architecture

With the use of service, there is a need to deploy an Elasticsearch service domain. A domain wraps hardware and software to run an Elasticsearch Cluster. The user can deploy that domain again through the console SDK CLI or Cloudformation.

Elasticsearch Instances

The Elasticsearch instances within the service come in two flavors; there are data nodes and master nodes. Data nodes hold data and respond to updates and queries, and the master nodes are orchestrators of the cluster.

API Conventions

The Elasticsearch REST APIs are accessed using JSON over HTTP, so it’s a restful API that supports HTTP. Elasticsearch uses the following conventions throughout the REST API:

Multiple Indices

              1)        ignore_unavailable
             2)        allow_no_indices
            3)        expand_wildcards

Data Math Support in Index Name

<static_name {date_math_expr {date_format | time_zone}}>

Common Options

Following are the standard options for all the REST APIs:

URL Based Access Control

(1)   multi-search

(2)   multi-get

(3)   bulk

Types of Elasticsearch APIs

Document API

Single Document API

Multi-Document API

Search API

The search API allows its users to execute a search query and get back search hits that match the query.

Multi-Index: The user can search for the documents present in all the indices or some specified indices.

Multi-Type: It allows to search all the documents in an index across all types or in some specified type.

URL Search: Various parameters can be passed in a search operation using, uniform resource identifier:

Aggregation

The Aggregation API collects all the data which is selected by the search query. This framework consists of many building blocks called aggregators, which help build complex summaries of the data. Here are some types of Aggregation API:

Index API

The index APIs are responsible for managing all the aspects of the index, such as settings, aliases, mappings, and index templates.

Cluster API

The cluster API is helpful to get information about the cluster and its nodes and make changes in them.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Amazon Elasticsearch Service

The majority of activities we do are driven by searching such as hailing a ride, getting support, or finding the file a user needs for the next meeting, and many more; this means everyone is a search expert with high expectations around how fast, easy, and rich search results should be. But building a great search for your customers and teams can be surprisingly complex with long project timelines and hefty costs. Machine-generated data is growing exponentially, and getting insights from it is important but complex for your business.

Amazon Elasticsearch Service

Elasticsearch service brings all search solutions together. Elasticsearch has emerged as a popular open-source choice to harness this valuable data but deploying, managing, and scaling elastic search can be challenging. Elasticsearch has covered enterprise-grade, modern search experiences.

Here we discuss how the Amazon Elasticsearch service works. Amazon Elasticsearch Service is a fully managed service that makes it easy for its users to deploy, secure and manage Elasticsearch clusters.

Elasticsearch Characteristics

Why Elasticsearch?

Benefits of Amazon Elasticsearch Service

Fully-Managed

It is a fully managed service that takes care of hardware provisioning, software installation, and patching failure recovery backups and monitoring. It supports elastic search open-source APIs and seamlessly integrates with popular data ingestion and visualization tools like Logstash, Cabana, and other AWS services (called ELK Stack) allowing users to use their existing code and tools to extract insights quickly and securely.

Fully Customizable

It provides a fully operational cluster customized to meet customer’s needs. It fulfills the fluctuating business demands without any downtime. The user can replicate data across multiple availability zones for higher availability, and the service creates daily backups for added data protection. It provides built-in encryption, so all the user data at rest and in motion are automatically encrypted. The user can use Amazon VPC and manage authentication to keep their data protected against hacking attacks and data loss.

No Upfront Fees

The Amazon Elasticsearch comes with a minimal price. There is no upfront fee or usage requirements; the user can also pay by the hour or save more by choosing reserved instances pricing.

Real-Time Analytics

Amazon Elasticsearch Service provides real-time analytics capabilities along with manageability.

Modern and Scalable

It provides modern search experiences that are simple to set up, scale with ease, and empower business users to own the search experience. Elasticsearch lets its users leverage all the power of Elasticsearch, complete with a refined set of APIs and dashboards, intuitive relevance controls, and robust analytics that make creating great search experiences easy.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

TheWhat is DataStax Enterprise?

When it comes to managing Big Data, modern businesses have to face many complex challenges. They need a database with scalability, high-performance, easy-to-use Interface, and cost-effectiveness. Modern companies need to implement a database quickly to handle the large volumes of real-time stream data and run analytics and enterprise search operations on the same data as quickly as possible to make business decisions. DataStax Enterprise is a complete big data platform built on Apache Cassandra architecture. It can manage real-time data modeling, analytics, and enterprise search queries.

The DataStax Enterprise 2.0

DataStax allows corporations to focus on delivering an exceptional experience and value while benefiting from DataStax’s commitment to platform innovation. DataStax Enterprise is an always-on, distributed cloud database built onApache Cassandra and designed for the cloud.

It provides a consistent data management layer, which means users can use it anywhere they deploy their application. The application cloud will be on the public cloud. It could be on a prem, on hybrid cloud deployment, or multi-cloud, but that does not matter with DataStax enterprise. It provides a consistent layer for these scenarios.  With DataStax, enterprise users don’t need to worry about re-architecting their application data layer again and again while moving from one infrastructure to another.

DataStax enterprise has three key features such as:

DataStax Enterprise Server

DataStax Enterprise Server inherits all the enterprise-class features of Apache Cassandra.

No need for manual ETL work

Easy Workload Re-provisioning

Multi-Data Center and Cloud Capable

DataStax Advantages for Enterprises

Functional Innovation

The limits of Apache Cassandra will not constrain DataStax Enterprise (DSE). The DSE roadmap provides the value-added capabilities needed by our customers. Differentiation will continue to increase in areas of performance, ease-of-use, and breath of platform.

Customer’s Success

DataStax will continue to provide across the broad development, training, documentation, services & support to deliver success for our customers.

Stability

DSE will continue to provide comprehensive testing, hotfixes for production issues, backward compatibility, etc., for the enterprise.

Lower Risk

Our customers will continue to drive growth in distributed scale requirements, responsiveness, hybrid environments, end-to-end security, and operations management.

DataStax Managed Cloud

A Fully Managed, Secure Architecture

Mixed Workloads

DSE also supports mixed workloads, so it offers database, analytics, search, graph, management monitoring, development tooling, etc. So, it’s a unified platform with a solution that provides all these solutions together in one package.

Multi-Model

DSE also supports multiple data models such as Tabular, key-value, JSON, and graph.

Advance Security

DSE provides enterprise-grade security, including identity management, unidentified authentication, operation, data encryption, data auditing, etc.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Managers have to make critical decisions from time to time with vast impacts on the organization. Providing access to the right information in the right moments empowers organizations to make the choices that drive their company forward. Amazon web services introducing Amazon QuickSight, a Cloud Power Business Intelligence service to facilitate this even more adequately.

Here we are providing an introduction to AWS QuickSight for business aspects.

AWS QuickSight delivers easy-to-understand insights to all the company employees. It connects an organization’s data wherever it is stored, including native AWS sources, spreadsheets, significant data sources, and third-party databases in the cloud. It also allows its users to interact on-premises and transforms it into rich interactive dashboards so they can freely explore and analyze information visually with a blazing fast speed.

AWS QuickSight Characteristics   

Scalable

QuickSight automatically scales with the user’s usage and activity with no need for additional infrastructure. From 10 users to 10,000, QuickSight seamlessly grows.

Serviceless and Fully Managed

QuickSight is a fully managed cloud application, meaning there is no upfront cost, software to deploy, capacity planning, maintenance, upgrade, or migrations.

Pay for What You Use

Pay monthly or annually, with Pay-per-Session pricing; data consumers only pay when they access their reports and dashboard with no up-front cost.

Fully Integrated

QuickSight is deeply integrated with user data sources and other AWS services like Redshift, S3, Athena, Aurora, RDS, IAM, Cloud Trail, Cloud directory, and more. Providing users everything they need for an end-to-end cloud BI solution.

Connect Organization Data, wherever it is

On-premises

Securely connect to the on-premise database and flat files such as:

In the Cloud

Connect to hosted databases, Big Data formats, and secure VPCs.

Applications

One Product for all Users

QuickSight covers all users from casual data consumers to dashboard creators to power users and analysts that need self-serve analytics.

Explore

Give power users and analysts the freedom to do their own-serve data discovery and analysis on governed data controlled by the organization.

Create

Create and publish rich, interactive dashboards to all the users.

Consume

With the new Reader Role, QuickSight allows its users to provide secure and easy access to everyone in the organization to interactive dashboards and reports on any device.

Explore, Visualize, Collaborate

Build Enterprise Ready

QuickSight has an AWS solution that can give any size business the power of enterprise-grade solutions. It provides an excellent solution for small teams and small companies, but it has an enterprise-ready route as an AWS solution.

Secure and Compliant

Global Availability

Enable collaboration across global teams, with local SPICE storage for optimized access.

Built-in Redundancy

It also provides native high availability and fault tolerance with transparent data replication and backups.

Data Governance

Create manageable datasets that provide flexibility to the end-user to perform self-serve analytics on data.

Create data set that:

Scalable, Secure Dashboard Publishing

QuickSight is optimized to deliver dashboards and reports to users across their organization in a secured, connected, and updated way because of its scalability and low cost.

 User Management and AD Integration

Microsoft Active Directory

QuickSight Enterprise Edition can integrate with an active user directory to dynamically manage users and groups.

 

Author: SVCIT Editorial

Copyright Silicon Valley Cloud IT, LLC.

Java Spring Framework Vs. ROR

Comparison Between Java Spring Vs. ROR

Here we are discussing the comparison between Java Spring Vs ROR.

Java Spring Framework

SourceForge started hosting Spring in January 2003 as an open-source community. It is ideal for Java Enterprise Edition. Spring is a complete and modular framework for developing enterprise applications in java. It is also known as an enterprise edition Java framework. Spring framework is flexible for all the layer implementations for a real-time application. Enterprises can also use the spring framework for the development of a particular layer of a real-time application.

Ruby Frameworks

Ruby is a dynamic, open-source programming language with a focus on simplicity and productivity. It provides an essential structure for web-based projects. Ruby on Rails is just for developing web applications with the minimum amount of extra dependencies. It is also known as a start-up-friendly development platform but lacks many quality attributes in software architecture design and hard to find recourse for development. Some of the best frameworks of Ruby are:

Spring Frameworks

Java Spring framework is a comprehensive tool for supporting applications using Java programming language. Spring also called the framework of frameworks because it provides supports to a various framework such as:

Features Associated with Java Spring Framework

Why Spring Framework

Spring is one of the most widely used Java frameworks for building applications for the Java platform. It aims to simplify Java development and helps developers be more productive at work. Unlike other frameworks, Spring focuses on several areas of an application and provides a wide range of features. One of the significant features of the spring framework is the dependency injection. It helps to make things simpler by allowing us to develop loosely coupled applications. This framework simplifies the Java Enterprise Edition architecture by reducing the complexity of the enterprise project management by directly using the Java Enterprise Edition technologies.  

Spring MVC is industrially adopted, and it is pretty robust. It provides the pattern and the structure for java enterprise applications. The main reasons for its popularity:

Why Choose Spring Framework Over Ruby for Enterprises Development

Companies Using Java Spring Framework

Companies Using Ruby on Rails (ROR)

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Angular Vs. React

Google developed angular, and its first release dates back to 2010. It is an open-source JavaScript framework. The first version of angular was also known as angular.js. Angular uses to develop large-scale rich featured applications, and Google maintains it. It is Ideal for web, desktop, and mobile platforms. It has a steeper learning curve.

React was developed by Facebook in the year 2011. React is useful for developing single-page applications and providing a view for MVC. It’s a JavaScript library that uses to develop UI or UI components. However, it is more famous for startup projects. Here we are going to discuss Angular Vs. React for Business aspects.

The architecture of Angular and React Framework

Angular and react both are component-based frameworks. Angular uses Typescript and HTML, whereas React is a user interface library, which uses JavaScript and JSX.

Creating project architecture based on React, will require multiple integrations and supporting tools for integration.

The Rendering Process

Angular performs the client-side rendering; however, it can be rendered on the server-side as well as using node.js; on the other hand, rendering is done on the server-side in the case of React.

Websites Built on Angular

The websites built using these frameworks are:

YouTube: The world’s largest video sharing platform owned by Google.

PayPal: The most popular online payment application website that is running on angular.

Walmart: The multinational retail corporation.

Gmail: Email service platform.

Websites Built on React.JS

Facebook: Facebook is the developer of React and Facebook, also uses React a well.

Instagram: Photo sharing platform.

WhatsApp: Cross-platform messaging app.

Airbnb: Here, people can book their stay for their vacations.  

Angular

   React

Testing

Angular

React

Business Benefits of Angular

Why Entrepreneurs like Angular for their Business

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

What is AWS Glue?

What is AWS Glue?

AWS Glue is a fully manageable ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize data, clean it, enrich it, and move it reliably between various data streams. AWS Glue’s design is ideal for working with semi-structured data. Here we are going to discuss how Amazon AWS Glue works for enterprise data maintenance.

 

When should we use AWS Glue?

We can use AWS Glue to organize, cleanse, validate, and format data for storage in a data warehouse or data lake. It also allows to transform and move AWS Cloud data into our data lake. We can also load data from disparate static or streaming data sources into our data warehouse or data lake for regular reporting and analysis.

To store data in a data warehouse or data lake, we integrate information from different parts of our business and provide a shared data source for decision-making and analysis.

Data Sources that AWS Glue Supports

AWS Glue supports at data stores:

Data Streams Supports by AWS Glue

AWS Glue Environment

AWS Glue calls API operations to transform our data, create run-time logs, store user’s job logics, and create a notification to help users monitor their job runs.

They can define AWS Glue jobs to accomplish the work required, such as extract data, transform and load data from a data source to a data target. Here the user performs the action for data store sources, defines a crawler to populate AWS Glue data catalogue with metadata table definitions.

It is faster, cheaper, and easier to use. Migrate to AWS Glue is 10x faster, and it is serverless means users do not need to worry about poisoning any cluster or server.

AWS Glue Usage

AWS Glue Benefits for Enterprise

Glue Data Catalog

AWS Glue has a data catalogue, so basically, it has all the metadata in the form of a database and tables.

AWS Glue Crawler

The crawler connects to a particular service to retrieve data; the service can be amazon S3, RDS, Redshift or dynamo DB, or any other JDBC connection. So, the crawler does it crawls through the data. For example:

Suppose an enterprise stores its data into a CSV file in S3 with like 100 million rows of data. The crawler infers the file’s schema, creates the tables, and stores it in the data catalogue. The data catalogue can then integrate with an S3 service to run the organization’s sequel queries to perform data analysis.

The AWS Data Glue catalogue can act as centralize metadata repository. This catalogue is not a database; it stores only metadata of tables such as table name, column name, and type of data. So, this metadata uses to create tables in AWS Athena. With AWS Athena, the user can run their SQL queries to perform data analysis on their organizational data.

Glue ETL Jobs

AWS Glue Components

Extract, Transform and Load

Glue Data Catalog

Crawlers

Workflow Management

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

 

High-Performance Team Culture

“SVCIT Recommended book to all employees”

Every leader wants their organization to be successful. But there are many definitions of success, such as creating great products, serving customers well, building an inspiring culture, or growth within profitability. Whatever the organizational definition of success is, every individual leaders’ responsibility is to achieve it, which begins with a strategic game plan on how to do just that. For any plan, we’ll need a playbook, and that is precisely what The Power of Playing Offense by Paul Epstein delivers in this national bestseller and top new release.  Positioned as a leader’s playbook for personal and team transformation, founded on the premise that “before we lead others, we must first lead ourselves.” With this democratization of leadership, we ALL can step into leadership.  No rank, role, or title necessary.  Just the desire to lead every day, starting by leading ourselves, so we can then most effectively lead others.

“Great leaders pay their dues because they don’t want their teams to pay.” This is the definition of servant leadership, which is completely aligned with the spirit of this book.

Leaders are the ones who make an organization successful primarily because how they lead is what is going to determine the performance and productivity of the team. Only if the leader is dedicated and accountable to lead by example and be willing to do the gritty work necessary is the winning formula that will ultimately lead to an effective team. A leader must have a vision, courage to take ownership, and nobility in management.

Grow from Purpose to Performance

Here we get inspiration from Paul Epstein’s book, The Power of Playing Offense.  Paul Epstein is the founder of Purpose Labs; after serving as an executive in the professional sports world for nearly 15 years in the NFL and NBA, coaching business teams through billion-dollar campaigns and breaking multiple Super Bowl revenue records. The secret sauce to this performance?  A purpose-driven culture—as he spent his entire career crafting best practices on what he calls a People360 blueprint, leading cultural transformations driven by purpose, performance, and impact—many of which are detailed in this playbook.  He now shares his methodology with us all.  One that helps business professionals and organizations achieve their goals through an action-oriented mindset of playing offense, where we take intentional steps each day to make a difference and build a legacy we’re proud of.

Importance of Strategy for High-Performance Team Culture

“Your title makes you a manager; your people will decide if you are a leader.”

– Trillion Dollar Coach of Silicon Valley, Bill Campbell

Strategy is a word that defines how an organization will achieve success, but executing a strategy depends on the organization’s culture. A positive and flourishing culture enables the strategy to thrive and sustain. Paul Epstein documents this connection between culture, strategy, and success in his book, The Power of Playing Offense, as he believes that leaders set the tone for culture, culture sets the tone for people, and people drive the performance of our business.  It is all connected—and it all starts with leadership.

Who Coaches the Coaches?

The main scintillation is to highlight the perpetual challenge of “who is coaching the coaches?”. Paul focuses on this significant point and provides excellent guidance for team leaders.

The Power of Playing Offense gives leaders a clear vision to accomplish their business goals, both internally with their team and externally with the marketplace relative to key stakeholders, brands, culture, products, and ultimately, profitability. It highlights key strategies to build business goodwill and high-trust environments—because, as Paul shares, when you win the inside game with your people, you win the outside game in the market.

In the foreword of the book, the Founder and CEO of ZOOM video communications, Eric Yuan, quotes: “we pride ourselves on a culture of care and are entirely intentional in bringing our culture to life. Paul’s authenticity and purpose shined immediately, and his approach meshed perfectly with our philosophy at Zoom: What makes people sense makes business sense.”

As Eric Yuan shares in the introduction of The Power of Playing Offense, Paul provides the roadmap to these key, game-changing principles, of which Zoom has blazed a trail for others in Silicon Valley to subscribe to:

Manager to Leader

In The Power of Playing Offense, bestselling author Paul Epstein designed a roadmap to transform managers into leaders to inspire their followers to manage their real-time issues and conquer their goals. His extraordinary thought for business team managers and leaders is to transform their teams from paycheck-driven to purpose-driven, from adversity to achievement, from disengaged to inspired, and from success to significance.

He observed that people with a playing offense mindset end up on top, and now he invites us all into his world to experience the journey with him, so we can all play more offense in our business, and lives.  Join Paul for this inspiring and action-oriented quest of purpose, passion, resilience, and impact.  With over 50 activities and exercises included in the playbook, we all have the ability to level up, as leaders of self, and our team.

Meet Paul at the 50, and consider him your coach for the journey.

It’s time to play offense!

“When you’re inspired with purpose, it’s all gas, no brakes,

and it never stops.”

PAUL EPSTEIN

To order The Power of Playing Offense, it is available on Amazon now

To contact Paul directly for coaching, consulting, training, or speaking opportunities, email paul@paulepsteinspeaks.com

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

CloudTrail vs CloudWatch

AWS CloudTrails and Amazon CloudWatch both services are cloud-based and providing logging capabilities. Amazon CloudTrail and Amazon CloudWatch are part of the management and governance category. Here we are discussing CloudTrail vs CloudWatch

 

CloudTrail vs CloudWatch

AWS CloudTrail

Amazon CloudTrail is the newer of the service and was launched in 2013. It is a service that enables governance, compliance, operational auditing, and risk management of AWS account. It is also known as an auditing system.  CloudTrail records the activity on the user’s account.

It provides events on the history of database account, including actions taken through the AWS Management Console, AWS SDKs, command-line tools, and other AWS services. This event history simplifies security analysis, resource change tracking, and troubleshooting. Besides, the enterprise can use CloudTrail to detect unusual activity in AWS accounts.  CloudTrail logs the following information with the minimum configurations such as:  

CloudTrail Benefits

Simplified Compliance

With AWS CloudTrail integration, an enterprise can simplify the users’ compliance audits by automatically recording and storing event logs for actions made within the AWS account. Integration with Amazon CloudWatch logs provides a convenient way to search through log data, identify out-of-compliance events, accelerate incident investigations, and expedite responses to auditor requests.

Visibility into user and Resource Activity

AWS CloudTrail increases visibility into the user and resource activity by recording AWS Management console actions and API calls. The enterprise can identify which users and accounts are called; the enterprise can also identify the source IP address from which the API calls were made and when the API calls occurred.

Security Analysis and Troubleshooting

AWS CloudTrail allows users to discover and troubleshoot security and operational issues by capturing a comprehensive history of changes in users’ AWS account within a specified time.

Security Automation

AWS CloudTrail allows tracking and automatically responding to account activity, threatening the security of the AWS resources.

Amazon CloudWatch

Amazon CloudWatch has its primary concern with what is happening with AWS resources so the user can respond to it. CloudWatch has metrics, alarms, CloudWatch logs, and CloudWatch Events. CloudWatch also helps to troubleshoot any issue and discover insights into the application that tackles any problem.   

Amazon CloudWatch is more established and providing the following functionalities, such as:

Amazon CloudWatch Functionalities

Metrics: A metric represents a time-ordered set of data points that are published to CloudWatch. A metric is a variable to monitor, and the data points represent the values of that variable over time.

Dimensions: A dimension is a name/value pair that uniquely identifies a metric. They can be considered as categories of characteristics that describe a metric. We can assign up to 10 dimensions to a metric.

Statistic: Statistics are metric data aggregations over a specified time. Aggregations are made using the namespace, metric name, dimensions within the time period specified by the user.

Alarm: An alarm can be used to initiate actions on behalf of users automatically. It watches a single metric over a specified time period and performs one or more specified actions.   

Monitor AWS CloudTrail Log Data in Amazon CloudWatch

CloudWatch provides the functionality to visualize and explore the CloudTrail logs, analyze the time-series log data, and create metric filters for organization data. Amazon CloudWatch is a monitoring and observability service with robust features that can help to drive actionable insights from vast amounts of CloudTrail log data.   

Resources Monitor by Amazon CloudWatch

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Why Need Amazon Elastic file storage?

What is AWS Elastic File Storage (EFS)

As lots of enterprise software applications require shared file storage which is accessible by multiple computers simultaneously. Here is the problem with building a personal file storage system; it takes time and can be costly. After file storage system deployment, it also requires complex maintenance and backup operations to ensure its performance is well and data is secure. To resolve all this complex maintenance, AWS provides Amazon Elastic File Storage System (EFS).

Amazon Elastic File storage is scalable file storage that can be useful with Amazon EC2. It allows an enterprise application running on multiple EC2 instances to access the file system simultaneously. The service uses the industry-standard NFS version for file access protocol.

Amazon EFS Attributes

Core Features of EFS

How is EFS Simple?

Fully Managed

Seamless integration with existing tools and apps

Elasticity 

Scalability

High Durability & High Availability

Who needs an EFS File System?

Enterprise who has an application or use case requires a file system with multi-attach systems, more throughput, availability/durability, and requires automatic scaling (grow/shrink) of storage need Amazon Elastic File Storage.

What are Customers using EFS for Today?

Access EFS File System via AWS Direct Connect

Amazon EFS file system also allows accessing files from within a VPC in AWS and on-premises servers via an AWS direct connect connection. AWS direct connect provides a private network connection between on-premises environments; AWS bypasses the internet entirely and improves latencies and throughput.

Direct Connect Support Addresses Three of Four Hybrid Scenarios

Understand Key Technical and Security Concepts

What is a File System?

Several Security Mechanisms

EFS supports action-level and resource-level permissions

Transferring Media Assets to EFS

Transferring many small files to EFS

Tools to use for Monitoring

DATADOG: Instance performance

Sumologic: Log Collection, Visualization

SALTSTACK: Command orchestration, Instance Configuration.

Amazon EFS has a Distributed Data Storage Design

Distributed file systems across unconstrained numbers of servers

Enables high levels of aggregate IOPS/throughput

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Microsoft Power BI integration

The Power BI is the market leader in solving data management crises. This tool is mainly aimed to visualize and organize the data. It is a great visualization tool; Power BI gives us a lot more than just visualization; it is a self-service business intelligence tool available in the cloud. Power BI has the incredible ability to aggregate, analyze, visualize and share data anywhere.  

Why Microsoft Power BI Integration

It is a business analytics service provided by Microsoft. Power BI provides interactive visualization with self-service business intelligence capabilities where end-users can create reports, dashboards and manage their data and databases easily and securely. Microsoft Power BI provides cloud-based BI services known as Power BI service and a desktop-based inference core power BI desktop. It offers data warehouse capabilities using data prep, data discovery, and interactive dashboards.

Microsoft released an additional service called Power BI embedded on its end-users cloud platform, enabling the users to analyze data quickly, perform various ETL operations, and deliver Power BI reports; the users can also share their reports anywhere.

Key Benefits of Microsoft  Power BI Integration

for Enterprises

Building Blocks of Microsoft Power BI

Visualizations: A visual representation of data is called visualization; for example, a chart or a graph can be used to represent data visually. Power BI provides different visualization types that keep getting updated with time; some of the commonly used visualizations are map representation, card visualization, stacked area, and Pie chart.     

Datasets: The dataset is nothing but a collection of data or information in the form of spreadsheets. It allows for pulling together data from many different sources like database fields, an excel table, and online results of some emails campaign to create a dataset.

Reports Server and Reports: Power BI’s report collects visualizations that appear together on one or more pages in a Power BI report. It is a collection of items that have a common motive. Power BI report server is an on-premise report server with a web portal that allows to display and manage reports. It helps to create Power BI reports, paginated reports, mobile reports, and KPI’s. The users can access and view those reports in different ways

Dashboard: A Power BI dashboard is a single-page interface that uses a report’s most essential elements to tell a summary of data.  

Tiles: In Power BI, a tile is a single visualization found in a report or dashboard. 

How is Microsoft Power BI Different?

Custom Visuals: Power BI provides custom visualization; it has opened up its SDK for visualization, which has given more custom visuals. It also has significant drag and drops features and data import capabilities.

Cost: Microsoft Power BI is very cost-effective as compared to other BI tools.

Integration: Power BI provides excellent integration capabilities because it readily integrates with various other tools and provides a scalable approach for enterprise tasks.

Data Management: For an enterprise, there are many concerns to manage their data, such as data sharing, data modeling, data filtering, shaping, and data analytics.  

Functionalities: Power BI is ideal for the overall organizational approach as a data visualization tool.

Take Power BI Content Offline: If the users find themself in a situation where they don’t have any network access, they can still view the dashboard data. All the data will get cached on the device.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

 

Amazon Lightsail Server

How Amazon Lightsail Server Supports Enterprises

Amazon Lightsail is the easiest way to launch and manage a preconfigured virtual private server with AWS. AWS Lightsail provides an easy way to launch and manage the private virtual server.

Here we are going to discuss how Amazon Lightsail Server Support supports enterprise business applications.

Why Amazon Lightsail Server?

When an enterprise business builds an application and chooses Amazon EC2 instance as a platform, they have the freedom to decide the underlying infrastructure like storage, server, security groups, IP address, VPC, etc., for their system. When an enterprise needs a preconfigured and pre-assemble system to run its application on a built-in platform, the best option would be a virtual private server.

However, a virtual private server provides a handful of fixed storage, fixed network configurations, and fixed infrastructure. Virtual private server (VPC) provides all these components. But the problem with the virtual private server is if, later on, an organization wants to expand their application, the virtual private servers provide minimal options for scaling; to solve this problem, Amazon, Lightsail comes into the picture.

The Amazon Lightsail server is very similar to the virtual private server. Still, unlike virtual private servers, it provides an option to auto-scale so it provides the simplicity of VPS backed up by the reliability and security of AWS.

The significant benefit of the Amazon Lightsail server is as an organization’s need grows regarding their business application Amazon Lightsail provides auto scale and connects other AWS resources like database and SMS services.

With Amazon, Lightsail users can run a simple application without bothering about the underlying infrastructure. The user can also have an option to order skilful resources according to their application needs.

Who Uses Lightsail and Why?

Here is everything an enterprise needs to jumpstart its project!

Easy to Integrate: With Amazon Lightsail no need to worry about underlying infrastructure like network and storage.

Powerful API: It allows integrating an application to other external applications using simple and flexible API.

Storage: It also provides highly available and high-performance storage in the form of an SSD block.

Secure Networking: Amazon Lightsail also provides speed and secure networking connections.

Additional Features: It provides easy to integrate additional features like database, content delivery network, etc.

Load Balancing: As an enterprise, applications have to handle lots of traffic, which can tremendously affect application performance. So, here the load balancing can help by distributing the load equally among different instances.

Instances: Application templates include WordPress, Drupal, Joomla, Magento, Redmine, LAMP, Nginx (LEMP), MEAN and Node.js, etc.

Cost-Effective: The Lightsail server is a virtual server that’s cost-effective, fast, and reliable with an easy-to-use interface.

How Amazon Lightsail is Different from EC2

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Introduction to AWS Elastic Block Store (EBS)

The AWS Elastic Block Store is a distributed, replicated block data store optimized for consistency, low latency, read and write access from EC2 instances. Amazon Elastic Block Store (EBS) provides block-level storage volumes for Amazon EC2 instances in the AWS Cloud. Each Amazon EC2 volume is automatically replicated within its availability zone to protect users from component failure to offer high availability and durability. It is specifically designed for service availability.

Why AWS Elastic Block Store (EBS) for Enterprise

It is a disk volume that can be attached to the EC2 instance. It is well suited for use as the primary storage for a file system, database, or any applications requiring regular updates and access to raw, unformatted, and block-level storage. EBS has the performance mechanism optimized at the base level. EBS provides highly available, highly reliable, and strange volumes.

Elastic Block Store Volume: Elastic Block Store volume is an additional feature that supports managing data easily. A volume can only be attached to one instance at a time, but many volumes can be attached to a single instance.

Elastic Block Store Snapshot: Snapshot can also be used to instantiate multiple new volumes, expand the size of a volume, or move volume across availability zones; snapshots can be shared using AWS Management Console or API calls. 

Elastic Block Store (EBS) Features

Types and Performance Measures of Elastic Block Volumes

Elastic Block Store Provisioned IOPS SSD (I01)

This is the best SSD back volume. It is the highest performance EBS storage option designed for critical I/O intensive database and application workloads. It is a high-performance SSD back storage type and provides good throughput per volume.    

Elastic Block Store General Purpose SSD (GP2)

It is the general-purpose SSD; the GP2 volume is the default EBS volume type for Amazon EC2 instances, suitable for a broad range of translation workloads, including dev-test environments, low latency, interactive application, and boot volumes. It provides a consistent baseline performance of up to 3 IOS per GB.

Throughput Optimized HDO (ST1)

Hard drives back ST1 is ideal for frequently accessed throughput intensive workloads, with a large dataset and large I/O sizes such as MapReduce, Kafka, Log processing, data warehouse, and ETL workloads. HDO also provides up to 250 Mbps per second of throughput per volume. It is a low-cost hard drive volume design for frequently accessed throughput-intensive workloads; it is ideal for large size companies.

Mostly it is used for big data, data warehouses, and log processing. An enterprise can use this when working with MapReduce, Kafka, log processing, warehouse, and ETL. The performance aspect will provide a baseline throughput of 40 Mbps per terabyte and a maximum throughput of 500 Mbps per volume.

Cold HDD (SC1)

SC1 is backed by hard disk drives (HDDs) and provides the lowest cost per GB of all EBS volume types. Enterprises can adapt this for less frequently accessed workloads with an extensive cold database. Its volume can burst up to 80 MB per second per terabyte.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Data Streaming with AWS Kinesis

AWS Kinesis

AWS Kinesis is one of the best-managed services, which significantly scales elastically, especially for real-time processing of the data at a massive point. These services can collect a large stream of data records, which are incredibly consumed by the application process that runs on Amazon EC2 instances. The amazon kinesis is used to collect, streamline, process and analyze the data to get a perfect insight and quick response to the information. Aws Kinesis also offers the key capabilities at a cost-effective price to process the streamlined data at a particular scale with flexible tools according to the needs and requirements.

Enabling Real-Time Analytics with AWS Kinesis

Data streaming technology enables a customer to ingest, process, and analyze high volumes of high-velocity data from various sources in real-time.

Key Components

AWS Kinesis provides an architecture that brings all of these components together. AWS provides several options for consumption from Kinesis data streams. Amazon Kinesis enhance Fan-Out allows for multiple consumers, each at 2MB/second independently. The user can get real-time data such as video, audio, application logs, and website clicks streams.

AWS Kinesis Advantages for Data Streaming

Real-Time: Amazon Kinesis enables us to ingest buffer and process streaming data in real-time to drive insights in seconds or minutes instead of hours or days.

Fully Managed: Amazon Kinesis is fully managed and runs the streaming applications without requiring users to manage complex infrastructures.  

Scalable: Amazon Kinesis can manage any amount of streaming data and process data from hundreds of thousands of sources with very low or minimal latency.

AWS Kinesis Capabilities

Kinesis Video Streams: The video streams are used to secure all the streams such as videos, photos, and the connected devices to the AWS for the machine learning analytics and other processing, giving access to all the video fragments encrypts the saved data without any problem.

Kinesis Data Streams: The amazon Kinesis data streams in amazon are specifically used to build the real-time custom model application by proceeding with the data stream process using the most popular frameworks. It can easily ingest all the stored data with all the data streaming costs by utilizing the best tools like Apache spark that can be easily run on the EC2 instances.

Kinesis Data Firehose: The AWS Kinesis is used to capture load and transform the data streams into the respective data streams; the Kinesis data firehose is useful to store in the AWS data store near all the analytics with all the existing intelligence tools. These tools are useful to set up all the loads continuously according to the destination with the durable for analytics, which gives an output like analyzing the streaming information.

Kinesis Data Analytics: Data analytics with amazon Kinesis is one of the best ways for an organization to process all the real-time techniques with SQL. It helps to capture the stream data that can run all the standard queries against the data streams to proceed with the analytical tools for creating alerts by responding to them in real-time.

Use Cases

Video Analytical Applications: AWS Kinesis is also used to secure all the streaming videos for the camera-equipped devices, which are placed in factories, public places, offices, and the homes to AWS account. The video streaming process is also used to play the video to monitor the security machine learning and face detection, and other analytics.

Batch to Real-Time Analysis: It also allows us to perform all the real-time analytical steps on the respective data to analyze the batch processing from the data warehouses using Hadoop frameworks.

Build Real-Time Applications: It also allows us to build real-time applications and monitor fraud detection.

Analyzing the IoT Devices: Amazon Kinesis helps its users to process the streaming data directly from IoT devices like embedded sensors, TV, set-top boxes, and consumer appliances. The user can also use this data to send real-time alerts to the action programmatically.  

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Sparx EA System Suite of Product

Sparx systems architecture platform is a comprehensive moulding environment, and enterprises using it predominantly to create models. It provides the pro cloud server, a web service that enables a seamless and secure connection to the underlying repository through HTTPS connections. Moreover, it provides the rest APIs that allow an organization to integrate the data into their enterprise architect model with external tools. The external tools cloud be the likes of service-now, CRM system, Jira, doors, etc.

Why Sparx EA for Enterprises 

Sparx EA eliminates a lot of overhead in bridging data between multiple toolsets. Sparx EA is providing Prolaborate, which is web-based and a front end for an enterprise architect.  It is mainly targeting Non-EA users. Its Prolaborate platform intends to utilize the information that the cloud be modeled in enterprise architecture and present it to the right audience.

Sparx Prolaborate

The Prolaborate is a sharing and collaboration software for the enterprise architect. Prolaborate bridges the business IT split by letting everybody collaborate on enterprise architecture models seamlessly from anywhere.

Seamlessly Share EA Models: The Sparx Prolaborate lets its users seamlessly share enterprise architecture models to the intended audience.

Efficiently Engage Non-Users: The Prolaborate efficiently engages non-EA users to review EA diagrams.

Foster Transparency and Agility: It provides foster transparency and agility in creating models.

The Prolaborate offers rich tools to greatly enhance the model viewing experience of the wider non-modeling community. It helps to transform rich EA models into an intuitive, live, collaborative portal in four simple steps such as:

Significant Capabilities of Sparx EA and Confluence Integration

Shirring architecture diagrams over digital knowledge management platforms such as Confluence and keeping them updated is essential in digital documentation. However, this has always been a manual effort; the new confluence integration from Prolaborate redefines this and enables its users to publish the live architecture information straight from their enterprise architect models to their confluence pages.

Sparx EA with Confluence integration will eliminate the manual effort needed to publish content and keep them updated. Sparx EA and confluence integration from Prolaborate enable three much-needed capabilities such as:

1.     Single Source of Truth

Prolaborate lets teams publish live, auto-refreshing architecture information in Confluence efficiently. The users will always see the current diagrams directly from the enterprise architecture models.

2.     Effortless Integration

The user needs to pick and choose the packages diagrams or elements from any connected enterprise architecture models from within Confluence using the simple, intuitive macro interface and publish them.

3.     Interface Views

It also brings the customized Prolaborate experience into Confluence. It allows users to interact and delve into the diagram contents’ details by seamlessly introducing the Prolaborate enterprises to enable further due diligence.

Why Sparx EA Prolaborate and Confluence Integration

Confluence can be used for different purposes, many enterprises using Confluence for technical documentation, which allows us to share the document with others for collaboration. The Sparx EA Prolaborate confluence integration enables the much-needed capabilities to integrate EA and Confluence. The user can share information from enterprise architect models within Confluence for once. It will significantly reduce redundant manual efforts and significantly improves efficiency. Also, helps to face challenges and to control access, maintain confidentiality, and publishing models. It provides contemplated a real-time agile way to engage users and seek their inputs and models. The confluence tool built for storing, sharing, and working on stuff such as:

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Why Splunk Enterprise?

Many organizations have a considerable amount of data to monitor to improve the system’s functionalities or generate business or technical analytics. There are different types of data, such as structured or unstructured data, from various resources such as business applications, systems, and clients generating data. Splunk Enterprise integration helps enterprises to manage and analyze data at a much faster and efficient rate. It is proprietary software that companies use to collect, analyze and monitor the data they produce.

Possible Data Resources of an Enterprise

Every enterprise has multiple data sources to monitor, such as:

These data sources can produce log files, metrics, messages, and audit messages. Here Splunk will gather this data, analyze and then visualize this data. So, it is an analysis and visualization tool.

Splunk Enterprise Integration for Enterprise Solutions

Splunk provides a wide variety of options for enterprises to configure; for example, it allows to monitor the logs, search across logs, create an index, stream all logs into a common location, search that particular log directory, and schedule reports. User does not need to login to the Linux machine that can be used directly, and users can search across machines or deployments.

If an organization is working on a micro-service environment or using lots of micro-services and they want to monitor logs, they can use Splunk. Once an organization streams all the logs to Splunk through forwarder, they can use common identifiers and search the logs across micro-services. Splunk users can stream the data in a real-time environment and use it for visualization.

Monitoring System Performance: Splunk enterprise can monitor a system to analyze how a particular system is doing whether its performance is efficient or not.

Data-Informed Decision: It will collect data to find meaningful insights to make decisions based on that data.

Security Cognizance: Splunk allows to find discrepancies and a security breach more efficiently in a different type of data.

Monitor & Notify System Health: One of the major benefits of Splunk is monitoring system health. Users can monitor the universal system that helps to monitor all the connected systems.

Improve Quality: Splunk enterprise also helps to improve the quality of products.

Why Log Analysis is Important for Enterprise

Logs are the go-to archives for gaining company-wide Operational Intelligence. An enterprise has lots of data from its users, applications, websites, and many servers to manage all these sources generates log files. These log files are not readable but contain the record of operations and transactions. These log files contain essential information such as customer IP address, geographical locations of visitors, and many more. It will also help detect network vulnerabilities.

System log files can help an enterprise understand and manage their system performance, CPU usage, CPU instances, which orbit software is running on the system, etc.

Splunk Solution for Log Files

Real-time Log Forwarding: Splunk is the ultimate log collection and analysis tool. It provides real-time forwarding of data and allows its users to visualize and get real insights from extensive data.

Real-Time Syslog Analysis: It provides real-time Syslog analysis, which is like a server analysis itself.

Real-Time Server Monitoring: It can monitor any application based on system logs generated in real-time and perform analysis. It also helps to monitor the IP traffic and client’s actions on the business application, etc.

Real-Time Alerts and Notifications: The user will get custom notifications when a security thread or something strange is happening to their servers. For example, if someone is accessing their network from an unreliable source, Splunk can send a notification alert if it is set up accordingly. Splunk will also provide alerts about system crashes, CPU usage, etc.

Historical Data / Log store & Analysis: The data that coming in real-time can be store in Splunk indexes, which is nothing but the database of Splunk, and Splunk also allows performing analysis on that database.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Custom API Integration in Slack

What is Slack?

Slack is a new layer of technology that brings together people, applications, and data. It is replacing email around the world. Slack turn the email into messages, and rather than inboxes, slack organize those message’s inboxes in channels. Those channels correspond to projects, teams, planning, office locations, and business units. A user can add the whole project’s team in the channel and anytime anyone on the team has an update at the same time and share information. Anyone can ask questions or share documents. Slack makes communication and collaboration better and easier. 

Custom Integration for Slack

Custom API integration in Slack is a type of application users can build and use on a single Slack team. The great thing about custom integration is that it provides a unique set of tools and an own workflow. Custom API integration allows us to customize notifications using webhooks.

What are Webhooks?

Webhooks are a simple direct, and secure method to send information from one application to another. A web app communicates one way. It immediately triggered what a defined action is taken; custom API with webhooks makes a web application more dynamic and flexible to notify other applications and communicate with other applications.

Example

Imagine a business organization onboarding a new client if a business company using Slack for internal communication. Instead of asking people to check back the platform, Users can use webhooks to establish a connect from their application to slack. When sales mark a client onboarding checklist, the webhook automatically sends an alert notification to the Slack channel. And account management follows up appropriately; it is almost like magic.

Slack and GitHub Integration

Users will get a notification on Slack when someone forks or stars their GitHub repository with Custom API integration and webhooks. It will provide information along with the sender/receiver name, repository link, and information. This scenario requires a webhook, a stateless server with configuration, and an incoming webhook in Slack.  

Slack API to Post Messages to Channel

Common Use Case

A company might have computed resources where they want to watch the CPU and memory usage. If the CPU goes above, for instance, then they can send a message to a dedicated slack channel automatically using python webhooks. 

Slack Custom Report Bot

Suppose a slack user wants to report errors or build personal analytics, or start custom passing different data types and information to a particular slack channel. In that case, they can use custom API webhooks. Whenever an error occurs, or someone uses their data from their particular web application, they will notify on the personal slack channel. Users can also connect their slack channel with their websites.

Security and Compliance with Custom APIs

If a company needs a compliant solution, the great solution is to move up to the enterprise grid. It helps identify, collect, and store any data in Slack and prevent sensitive data loss using Custom API Integration. It is possible to create APIs that can allow the administrators within a slack organization to discover and secure any of the data in a slack org. This includes public channels, direct messages, private channels, and even files uploaded to slack.

Connect Multiple Tools using Custom API Integration

Many organizations facing this difficulty to manage their clients on different types of platforms that are not easy to manage. They need to handle many tools to communicate with all clients and share work-related information on all different tools one by one; it is a time consuming and costly process.

It is possible to manage clients with different types of tools such as WebEx, MS teams, Zoom etc. by using one Slack channel with Custom API Integration. Custom API Integration makes it possible to communicate directly with a person using Slack with another person using MS teams.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Microsoft Teams & Salesforce Integration

Microsoft Teams is a chat-based workspace; where co-workers and colleagues can create and make decisions as a team. It allows chatting, meet, share files, and work with others. Business applications make it a central software hub for communication and collaboration, whether business teams are office-based or working remotely. Microsoft teams can accommodate seamless collaboration by bringing everything together into one shared workspace.

Benefits of Microsoft Team

Enabling Effective Communication

Microsoft teams allow group and private messaging with threaded conversations. Users can create different channels to organize their communication by topic. It is a real-time chat function that enables us to store brainstorming sessions, conference calls, and other meetings into one easy to find place.  

Anywhere, Anytime on Any Device

It can be used anywhere anytime on any device. As a cloud base platform, the Microsoft team is accessed everywhere by a desktop or mobile application. It is supported on Windows, Mac, and IOS, and android.

Increase Productivity

If a company uses traditional email to work on a project, it may lose crucial information in the mountain of email threads. However, with Microsoft receiving the same message simultaneously, people can collaborate and keep the discussion flow in helping to reach out the solutions faster in a more organized structure. Moreover, if a new team memberjoins, they can access prior conversations with instant access to all the project-related files.

Office 365 Integration

Microsoft teams are fully integrated into Microsoft office 365, including Word, Excel, Skype, SharePoint, and PowerPoint.

Synced Meeting

Microsoft teams sync the calendar for important existing appointments, suggest a time when the other attendees are free. It gives the option to choose if the private meetings are open; attendees can post the meeting in separate chat threads, set agendas, and upload relevant documents. It also allows us to schedule and join meetings.

Work Better Together

With Microsoft teams, everyone can work on the same document simultaneously. Users can edit the document while logging chats around the content; it combines chat meetings, notes, and attachments allowing teams to interact with each other seamlessly. Calendars, files, and emails can also be shared.

Customize Workspace

Microsoft teams allows to integrate third-party tools and any Microsoft applications.

Faster Processes

MS teams provide speed through tasks faster and share easier with a helpful set of commands. It is an excellent tool for both businesses and creators who work collaboratively with people worldwide.

Microsoft teams leverage chatbots on teams and provide functionalities to make business tasks easier; here is the list of some features such as Marketing, Sales, HR, Engineering, Direct access to support, finance, and accounting applications.

Microsoft Teams and Salesforce Integration with Automate I/O

Every new lead generated by a business is an opportunity to grow. Automate. I/O helps avoiding any information slip away. Automate. I/O is a simple tool that connects web applications and automates tedious manual work. It provides encryption for users’ data. It allows us to connect the Microsoft team and Salesforce accounts.

Microsoft team is a new way for companies to do business with over 70% growth. Create incredible customer experiences delivered to business’s team device with total visibility of all team’s call and data automatically in Salesforce.

Salesforce automation works like a trigger application that starts a bot to run new processes. The bot will trigger whenever there is a new lead in Salesforce.

Scalability

Users can modify the integrations according to their needs and get notified of new contacts or opportunities. Salesforce allows to track performance and make data-driven decisions with the flexibility to work; it is a one phone platform to manage.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Kubernetes Service

What is the Kubernetes Service?

Kubernetes service is an open-source container orchestration framework that Google develops. It manages containers such as Docker containers. Kubernetes helps to manage containerized applications made of hundreds or thousands of containers. It also helps manage them in different environments such as physical machines, virtual machines, cloud environments, or even hybrid deployment environments.

What Problem does Kubernetes Service Solve?

The need for a Container Orchestration Tool

The more use of microservices increases container technology practice because the containers offer the best host service for small independent applications such as microservices. To manage those loads of containers across multiple environments using scripts and self-made tools can be really complex and even sometimes impossible, so that specific scenario causes the need for having container orchestration technologies.

Features Offer by those Orchestration tools     

High availability or no downtime: High availability means that the application has no downtime, making it always accessible. 

Scalability or High Performance: Scalability means that the application has high performance. It loads fast, and the user has a very high response rate from the application.

Disaster Recovery: If the infrastructure has some problems like data loss, server corruption, or something terrible happened with the server center. The infrastructure must have some mechanism to back up the data and restore it to the latest state. So, the application doesn’t lose any data. The containerized applications can run from the most recent state after the recovery and all of these functionalities that container orchestration technologies like Kubernetes offer.

Essential Fundamental Components of Kubernetes Service

Kubernetes Service has tons of components; some of them are mentions here:

POD: The simple server or a virtual machine and the basic or smallest component is POD. It is an abstraction over a container. POD component allows to creates a running environment. It is an application that has its container and database. It is usually meant to run one application container inside of it. This component allows running multiple containers inside of it. But usually, it is only the case if the user has one main application container and a helper container or some side service that has to run inside of that POD.

How Containers Communicate?

Kubernetes service offers out of the box a virtual network, which means each POD gets its IP address, not the container the POD gets the IP address, and each POD can communicate with each other using that IP address.  If a database is crashed, a new database will automatically create and assign a new IP address.

Services

As mentioned before, in a Kubernetes cluster, each POD gets its internal IP address, but the PODs in Kubernetes are ephemeral, which means when a POD will restart, the old one will die, and the new one get started in its place and gets a new IP address.  The service provides a stable IP address that stays even when the POD dies. Service also provides load balancing because when there is a POD replica, for example, a user has three replicas of a microservice application. The service will get each request targeted to the microservice application and then forward it to one of those PODs. So clients can call a single stable IP address instead of calling each POD individually.

The Kubernetes services are a useful abstraction for loose coupling for communication within the cluster.   

Here are some different types of Kubernetes services, such as:

Cluster IP Service: This is the default type of service, which means when a user will create without specifying a type, it will automatically take cluster IP as a type.

Headless Service: It is a simple service with no cluster IP address. Headless service not providing low balancing or proxying. Its job is to create and maintain DNS records for each of the PODS.

NodePort: It is part of mobility service resources only. It opens a specific port on the node.

Load Balancer Service: This service refers to efficiently distributing incoming network traffic across a group of backend servers/microservices.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Every organization wants to choose the best platform to run their business to create their customers’ best experience. In the past, DevOps tools kept teams siloes in different groups making it hard to align their priorities and deliver successfully but this has changed with CloudBees Jenkins enterprise.

CloudBees Jenkins Enterprise

CloudBees Jenkins enterprise platform provides a range of continuous integration delivery solutions powered by Jenkins that meets enterprises’ unique needs on-premise or in the cloud. It improves business agility and business-IT alignment by delivering more structured software. CloudBees Jenkins simplified the user experience to make it easier to manage Jenkins for faster delivery and onboarding teams in a minute. So, they can get aligned and focused on projects. CloudBees Jenkins provides a new UI that offers simple to create teams as developers get pulled into a new project. Organizations wanted an intuitive way for administrators to set up teams in a scalable fashion. So, developers never need to wait to build their projects. Security comes pre-configured for many roles, and Jenkins is secured out-of-the-box; this means faster audits and less time configuring security settings.

CloudBees Jenkins for Enterprise Companies

With CloudBees Jenkins, enterprise companies can ship software in a repeatable fashion, and teams have the flexibility to choose the right tools to get the job done.

Key Features at a Glance    

CloudBees Jenkins Enterprise Continuous Delivery for the DevOps

Built-in Scale

Standardize processes to support multiple teams and improve collaboration. Infrastructure costs are reduced with built-in elasticity and multi-tenancy.

Easy to Manage

Resilient by Design

Security and Compliance

Easy Installation

Jenkins is a self-contained Java-based package for Windows, Mac OS, and Unix OS.

Easy Configuration

It provides easy setup and configuration via its web interface, including error checks and built-in help.

Plugins

It has hundreds of plugins in the update center and integrates with every CI and CD toolchain. 

Extensibility

Jenkins can be extended via its plugins architecture and provides nearly infinite possibilities for what it can do.

Distributed

It allows its users to distribute work more efficiently across multiple machines, helping in faster building, tests, and deployments across multiple platforms.

Create a Pipeline

A pipeline connects to a required repository; with a few clicks of a button, choose a project. Here project files are seamlessly integrated into the build. Users can even connect pipelines from different applications so teams can work in parallel on the latest deliverables. CloudBees make it easy for teams to focus on delivery with enterprise features according to an organization’s need.

CloudBees Jenkins offers solutions to scale enterprise essentially. Jenkins also manages security capabilities for business data. It is empowering teams to deliver software at the speed of ideas.   

CloudBees Distributed Pipeline Architecture

CloudBees distributed pipeline architecture reduce business risk through:

Versatility

CloudBees Assurance Program (CPA)

Key Takeaways

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Simple Notification Service (SNS) vs. Simple Queue Service (SQS)

What is AWS SQS (Simple Queue Service)?

Amazon Simple Queue Service (Amazon SQS) is a distributed message queuing service. It supports the programmatic sending of messages via web service applications to communicate over the internet. SQS is intended to provide a highly scalable hosted message queue service that resolves issues arising from the common producer-consumer problem or connectivity between producer and consumer. It supports standard Queue and FIFO queue. It is a service that handles the delivery of messages between components.

SQS processes a large number of messages without losing a single message with less hardware configuration.

SQS Components

Producer: Producer components of users’ application architecture are responsible for sending messages to Queue. At this point, the SQS service stores the message across several SQS servers for resiliency within the specified gear; this ensures that the message remains in Queue if a failure occurs with one of the SQS servers’.

Consumer: The consumers are responsible for processing the messages within the key when the consumer element of users’ architecture is ready to process a message from the Queue. The message is retrieved and marked that the process has been processed by activating the visibility timeout on the message. This timeout ensures that the same message will not be read and processed by another consumer. When the message has been processed, the consumer then deletes the message from the Queue.

Visibility Timeout

When the consumer retrieves a message, the visibility timeout is started. The default time is 30 seconds. It can be set up for as long as 12 hours. If the visibility timeout expires, the message will become available again in the Queue for other consumers to process.

What is AWS SNS?

SNS is a notification service provided as part of Amazon web services. It’s a Message publishing and processing service (PubSub). This service provides a low-cost infrastructure for the mass delivery of messages, predominantly to mobile users. From the sender’s viewpoint, SNS acts as a single message bus that can message various devices and platforms. Simple Notification service SNS can also deliver messages to 200+ countries. SNS uses the publish/subscribe model for push delivery of messages.

In AWS SNS, there are two types of clients

Topic

An Amazon SNS topic is a logical access point that acts as a communication channel. A topic lets its users group multiple endpoints (such as AWS Lambda, Amazon SQS, HTTP/S, or an email address).

Subscribers

Subscribers (web servers, email addresses, Amazon SQS queues, AWS Lambda functions) consume or receive the message or notification over one of the supported protocols (Amazon SQS, HTTP, email, SMS, Lambda) when users subscribe to a topic.

Technical Comparison

Simple Notification Service Simple Queue Service
SNS stands for simple notification service SQS stands for simple queue service
Publisher / Subscriber System Queueing service for message processing
Publishing messages to a topic can deliver to many subscribers of different types (SQS, Lambda, Email) A system must poll the Queue to discover new events. A single consumer typically processes messages in the Queue.
Scale Automatically Scales Automatically
Keep messages secure using AWS KMS keys Keep messages secure using AWS KMS keys
Messages can go to different subscribers based on fields in the message (Message Filtering) Reliable, Dead letter Queues can be enabled
Fan Out Architecture – The same message can be consumed by multiple consumers Convert synchronous pattern to asynchronous. One message cannot have multiple consumers. Once a message is processed by the consumer, it gets deleted from SQS.
SNS is centered around topics. User can use the topic as a group for collecting messages SQS is a fully managed service that works with a serverless system, microservices, and distributed architectures
Users or endpoints can then subscribe to this topic, where messages or events are published It has the capability of sending, storing, and receiving messages at scale without dropping message data
When a message is published, all subscribers to that topic receive a notification of that message It is possible to configure the service using the AWS Management Console, the AWS CLI, or AWS SDKs.

If you have any questions about integrating AWS Simple Notification Service (SNS) and Simple Queue Service, feel free to contact Silicon Valley Cloud IT professionals for a free consultation.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Microsoft phone system is a cloud phone system integrated into office 365; it uses Microsoft Team as the client app. It can replace common phone system users using regular desk phones and applications on the computer. There is no need to worry about upgrades, maintenance, or complex configuration with the Microsoft phone system integration.

 It is innovation at its best, and it is built on three key elements, such as:

Modern Operations: It is a cloud-born purpose-built generation team infrastructure.

Globally Resilient Meeting Performance: It is built on the Microsoft global media delivery network.

Actionable Insights: Cognitive services and machine learning deliver insight actions into calling and meeting experiences, which enable Microsoft to improve that media quality and overall user experience.

Why use Microsoft Teams for Phone Calls?

Microsoft Phone System Stability

It is highly scalable that users have session board controllers who make sure that users get the best quality voice.

It is also providing unique telephone numbers or port existing telephone numbers. Microsoft Phone system provides all those advanced features that users would expect in a phone system apart from a contact center.

Microsoft Phone system providing the following features such as:

Elements to Microsoft Phone System

How to Deploy Microsoft Phone System for A Company?

Here are some ways to deploy Microsoft teams phone system for a business company, such as:

Click-to-Dial (outbound) Integration within the Teams App: It is a simple integration with a cloud PBX service provider using a click to dial.

Integration with Teams via Call2Team: It integrates with Microsoft teams’ phone system and is typically fueled by an application called Call2Team. So, call PBX service providers are partnered with a call to Microsoft Teams. It allows its users to use their cloud PBX service providers’ features and functionalities.

Purchase Teams Phone System and a Calling Plan Through Microsoft, directly: It can be purchase by Microsoft directly; the users can purchase the team’s phone system application and calling plan.

Direct Routing as a Service: The user will not require a manager to manage their own SPC and all quality.

Silicon Valley Cloud IT experts will suggest to their clients the best option for their company and provide complete integrations.

Online Presence

The users can manually set their online presence. It also set automatically when:

Contacts

Online Meetings

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Introduction to Amazon Virtual Private Cloud (VPC)

Amazon Virtual Private Cloud (Amazon VPC) enables its users to provide a logically isolated section of amazon’s web service cloud where users can launch amazon web services (AWS) and resources into a virtual network. Users will have complete control over their virtual networking environment, including selecting their IP address range, creating a subnet, and configuring route tables network gateways. This virtual network closely resembles a traditional network that the user has to operate in its own data center, with the benefits of using the scalable infrastructure of AWS.

Way Amazon Virtual Private Cloud?

The three most essential benefits provided by Virtual Private Cloud are privacy, security, and prevention from loss of proprietary data. It does not just give the security, but it also provides the ease of connecting to their instances or services. 

When a user wants to create an AWS credential, a default virtual private cloud is created, but in industry or at the production level, an organization needs to customize its virtual private cloud. AWS provides the tools required to customize Virtual Private Cloud.

Default VPC Custom VPC
The default VPC is created by AWS when the user will create a new account. The Non-default VPC is created and configured by the user to an EC2 instance.
All advanced features provided by EC2-VPC are preconfigured. User must explicitly create a subnet, NAT, security group, internet gateway, etc.

What is an IP Address?

An IP address is a logical, numerical label assigned as a unique entity to each device in a network. It is beneficial to locate the host in the network through the Network ID and Host ID present in the IP address.

Amazon VPC Terminology

Here are some terminologies that are used in a virtual private cloud (VPC), such as:

Subnet

A range of IP addresses in a Virtual Private Cloud is called a subnet. AWS resources can be launch into a subnet selected by the user. Users can use a public subnet for resources connected to the internet and a private subnet for resources that won’t be connected to the internet.

Key Components of VPC

Internet Gateway and NAT: It is logically enables routing of traffic in the public network.

DNS: Standard DNS, which resolves names used over the internet into IP address. 

Elastic IP: It is a static IP that never changes (supports only IPv4).

VPC Endpoints: Private connection between users’ Virtual Private Cloud and other AWS services without using the internet.

VPC Peering: Connection between VPCs

Route Tables: Defines how traffic is routed between each subnet.

Egress only IG: It allows only outbound communication from EC2 over IPv6.

Network Interface: A point of connection between a public and a private network.

VPC Benefits for Business Applications

VPC provides advanced security features such as security groups and access control lists to enable inbound and outbound filtering at the instance and subnet levels. Users can also store data in S3 and restrict access to be only accessible from instances in VPC. User can choose to launch dedicated instances which run on hardware dedicated to a single customer for additional installation. AWS management console helps to create and launch VPC more easily. Additionally, it provides all the same benefits as the rest of the AWS platform regarding scalability and reliability. Amazon VPC provides a variety of connectivity options to connect Virtual Private Cloud to the internet, too; the data center or other VPC is known as VPC peering based on AWS resources that users want to expose publically or privately. The connectivity options are as follows:

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Elastic Compute Cloud is a web service that aims to make life easier for business organizations by powering secure and resizable compute capacity in the cloud. It is very easy to scale up or scale down our infrastructure based on demand. Amazon elastic compute cloud (EC2) is an elastic virtual server running inside the AWS cloud. AWS also helps construct and operate the data centers to put all the equipment in those data centers that users need to connect and access. Amazon Elastic Compute Cloud EC2 is one of the most popular AWS offerings.

EC2 mainly consists of the capability of the following functionalities, such as:

What makes Amazon EC2 different?

What makes Amazon EC2 different is that users use only the capacity that they need. Amazon EC2 eliminates the user’s need to make large and expensive hardware purchases, reduces the need to forecast traffic, and enables users to immediately deal with changes in requirements or spikes in popularity related to users’ applications or services.

EC2 Instances Overview

Instances have five characteristics advertised on the website:

Elastic Compute Cloud (EC2)

EC2, the elastic compute cloud, makes it easy to access the following services such as:  

Amazon EC2 Instance Families

General Purpose: M1, M2, T2

Compute Optimized: C1, CC2, C3, C4

Memory-Optimized: M2. CR1, R3

Dense Storage: HS1, D2

I/O Optimized: HI1, I2    

GPU: CG1, G2

Micro: T1, T2

EC2 Performance Factors: Networks

AWS Proprietary, 10Gb Networking

Enhance Networking 

EC2 Performance Factors: Storage

Benefits of Amazon EC2

Elasticity: EC2 gives the ability to add and remove instances to work on the applications’ demand elasticity to save money. It helps to handle cyclical and unexpected demands.

Scaling Automatically

Amazon EC2 provides automatic scaling. It provides services that can add servers automatically to a group to support the increased load. Users can monitor CPU across the fleet, and its CPU levels across the fleet raised above 80%; the user can add another EC2 instance.

Completely Controlled

Flexibility

Customers also like the flexibility to run any application on the hardware configuration operating system they desire. With EC2 flexibility, users can choose which operating system they want to use, for example, Linux or Windows. It allows choosing instance type for CPU, Ram, or disk combination. Users can also stop an idle instance, add storage required in terms of volume and performance.

Security

Elastic Compute Cloud provides high-security premises, secure access, built-in-firewalls in the form of security groups, and unique users. The Amazon EC2 also offers multi-factor authentication, private subnets, encrypted data storage, and direct connect service.

AWS Global Infrastructure

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Simple Storage Service

Companies are finding a way to store, distribute, and manage all of their data, which is a big challenge. Running applications, delivering content to users hosting high traffic websites, or backing up documents database and email will require a lot of storage. More storage space keeps growing every day. Building and managing a personal repository is expensive and time-consuming.

The user has to buy the racks of dedicated hardware and software and then get it all up and run; the user needs to hire staff to set up complex processes to ensure that the storage performs well and backs up is stored if something fails. Add more capacity costs and time to deploy more servers’ hard drives and tape backup machines. To predict how much capacity they need in the future is difficult; they may have the storage they need or overspend and end up with excess capacity that makes their sits idle.  

Amazon Simple Storage Service (S3) provides developers and IT teams with safe, secure object storage. It is easy to use with a simple web services interface that helps store and retrieve any Amazon EC2 or anywhere on the web. Users need to choose the region where they want to keep their data.

The user needs to create a bucket, and they can begin storing data in Simple Storage Service. With Amazon Simple Storage Service, the user doesn’t need a crystal ball to predict how much storage they will need in the future. An organization can store as much data as they want and access their data when they need it.

Data Recovery Backup and Archiving

Companies always worry about losing their valuable data; Amazon Simple Storage Service helps store and manage more backups and archives. Amazon S3 is ideal for this purpose. Users can keep a practically unlimited amount of data for when its need for traditional IT infrastructure.

Durable  

Amazon Simple Storage Service (S3) is exceptionally durable, and it provides 1109 ability. Data is stored across multiple facilities and multiple devices within each facility.

Available

Amazon Simple Storage Service is designed for 99.99% availability. Users can also choose the AWS region in which to store their data. It also allows its users to optimize latency, minimize storage cost, and address regulatory compliance. Suppose an organization has sovereign data that needs to be in a specific country user can choose an amazon region that satisfies that need.

Cost-Effective

Amazon S3 is also a cost-effective user that can store huge amounts of data at a very low cost and only pay for what they use.

Security

Amazon Simple Storage Service is also very secure as it supports SSL data transfer and data encryption once it is being uploaded. It is also providing access control to data using IAM. It is also specifying object permissions using the S3 policies.

Scalable

Amazon S3 is also highly scalable; it allows storage of as much or as little data according to its users’ needs. Here storage is elastic, so the user can scale up and down as required and only pay for what they are using.

Notification    

It also allows configuring notifications when objects are loaded into Amazon S3 by SQS, SNS, or even Lambda. In this way, it is easy to set up workflows for files.

High Performance

Amazon Simple Storage Service is also a highly performant user who can use multi-part uploads to maximize network throughput and resilience. Amazon S3 transfer acceleration is a new service that uses edge locations to increase upload and download time.

Integrated

Amazon S3 is also fully integrated with many AWS products such as CloudFront, CloudWatch, RDS, EBS, Lambda, etc.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon WorkDocs

In today’s global enterprise, everyone needs to work anywhere and everywhere on all kinds of devices and do that effectively; the user needs to access and share files. Moreover, Amazon WorkDocs provides the solution by the secure leading industry AWS cloud. WorkDocs is designed to fulfill the needs of the global enterprise. It is a fully managed file share and storage service that allows storing, sync, and sharing files from any location.

On-demand Access: With Amazon WorkDocs, the user always has On-demand access to the latest version of their work. The user can easily view all major file formats.

Strong Admin Controls: Amazon WorkDocs provides near real-time visibility into all activities and strong admin controls to determine who accesses the content.   

Add More Users at any time: It allows us to add more users at any time.

WorkDocs is fully integrated with other AWS services and business productivity applications, including amazon workspaces, reducing complexity and monitoring cost.

Amazon WorkDocs Capabilities

WorkDocs capabilities are all about how customers think about content collaboration platforms.

Foundational Capabilities

Library and Repository Services: Amazon WorkDocs is a hugely scaled and reliable repository service built on top of Amazon S3.

Search: Moreover, AWS has enabled an advanced search facility that allows users to search using file and folder names and do a content search for metadata values.

Security: Security is embedded in the form of integrated security for authentication using Microsoft active directory and standalone authentication for external sharing use cases.

Metadata Services: It enables customers to add custom metadata values that can be used to categorize files and search on these metadata values to retrieve files.

Workflows: There are simple workflows available to enable scenarios such as approval workflows for document approvals and contract negotiations.

Analytics: For analytics amazon, providing a rich set of APIs allows customers to build detailed reports on user activity, file usage activity, and API access from the cloud.

Extended Services for Business Applications

Intelligent Content Services

WorkDocs Use Cases

Amazon WorkDocs is a very versatile platform and can support a wide variety of content management use cases. However, four key use cases work very well with end-to-end without additional work needed from customers. An organization can use the Amazon WorkDocs site, drive, and mobile application to enable a modern file sharing experience for its users. Here are four use cases the user can enable:

File Repository in AWS Cloud

File Collaboration

User Share and Team Share Replacement

Build Applications

WorkDocs Features

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Companies are increasingly going digital, and modern applications utilizing microservices and service architectures to scale, serve a global customer base, and gain release velocity. In this world of business, application success means better results. However, monitoring distributed applications and resources is challenging because of their complex data overload and the fact that monitoring tools were built to oversee system and application performance and physical silos to gain observability. A shift from looking for failures defining answers is the key solution is Amazon CloudWatch.

Amazon CloudWatch

Amazon CloudWatch service is a monitoring service for AWS cloud resources and the applications to run on AWS. It is built for development, system operation, website reliability engineers or SRE, and IT managers. Amazon CloudWatch is the Amazon Web Services component that provides real-time monitoring of AWS resources and customer applications running on Amazon infrastructure. AWS Cloud watch enables monitoring for EC2 and other Amazon cloud services. It provides alerts when things go wrong. Users can use Amazon CloudWatch to collect and track metrics to get system-wide visibility into resource utilization application performance and overall operational health.

The user can use the insights to react and keep the application running smoothly. It is also providing data and actionable insights to monitor the application, analyze and respond to system-performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects data in the form of logs, metrics, and events. It is providing a unified view of AWS resources applications, services, and on-premise servers.

What Does Amazon CloudWatch Do?

Resources Monitored by CloudWatch

Some resources monitored by AWS CloudWatch are as follows:

CloudWatch Monitoring

Amazon CloudWatch offers two types of monitoring, such as:

Basic Monitoring

CloudWatch basic monitoring included free of charge and polls every five minutes and gives ten metrics, five gigabytes of data ingestion, and five gigabytes of data storage.

Detailed Monitoring

CloudWatch detailed monitoring is chargeable. Its price is per month, but it pulls every minute, so if the user wants more detailed monitoring, they can pay for it.

Metrics

AWS CloudWatch allows recording metrics for services such as EBS, EC2, Elastic load balancer, and Amazon S3. These matrices provide visual and text-based notifications. Metrics are hypervisor level so that the user can get things like CPU disk network.

Events

Amazon CloudWatch monitoring allows creating events based, for example, trigger Lambda functions.

Logs

Users can install agents on EC2 instances to send monitoring data about the instance to CloudWatch. With this, the user can monitor things like HTTP response codes in Apache, or the user can count exceptions in application logs.

Alarms

It allows set alarms to warn based on resource usage; for example, if CPU utilization is too high, it can also send notifications. It can help auto scale, so if the CPU is maxed out user can get another instance launched to take care of some of the load, or the user can send cloud watch monitoring alarms to EC2 actions to recover or reboot an instance. It also allows using alarms to shut down the instance. When instances are idle, then cloud watch allows to shut them down.   

CloudWatch enables automated actions to troubleshoot issues and discover insights to optimize applications, and they are running smoothly. With Amazon CloudWatch it is easy to get started there is no upfront commitments or minimum fee user pay for what they use. It is a smart way to monitor a business flexibly at a low cost.

If you have any questions about AWS Cloudwatch, feel free to contact Silicon Valley Cloud IT professionals for a free consultation.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Redshift

Customers want to leverage more modern data architectures to deliver analytics on a broader range of data and toolsets. Customers also want to reduce the cost and complexity of operating a traditional data warehouse to provide all the facilities. Amazon brings Redshift for the enterprise data warehouse.

Amazon Redshift enterprise solution is a fully managed data warehouse service from AWS that is easy to use and very cost-effective; it allows to run complex queries against petabytes of data, and most results come back in seconds.

Amazon Redshift Enterprise – 10x Faster at 1/10th the cost

Amazon Redshift enterprise solution is fast as it delivers ten times better performance than on-premises data warehouses. It is also fast for all types of workloads, from short-running queries to complex, long-running queries on a trillion data rows. Amazon Redshift enterprise solution leverages a massively parallel processing architecture to deliver high throughput. Redshift enterprise solution allows the creation and start of a data warehouse quickly.

Amazon Redshift enterprise also automates most common administrative tasks to manage, monitor, and scale a data warehouse, including backups, updates, and more.

It also allows building quick integrated data lake and analytics with Amazon Redshift enterprise. Many enterprises in the financial services, healthcare, and retail government trust amazon Redshift enterprise solution to run mission-critical workloads and keep their data secure.

AWS Database Migration Service (DMS)

AWS database migration service providing the following facilities:

When to use AWS DMS

AWS SCT Data Extraction Agents

Extraction agents can be installed on:

Agents support the following source data warehouses:

Data Warehouse Offload Tasks

Amazon Redshift

AWS Snowball

AWS Snowball is a petabyte-scale data transport solution that uses secure appliances to transfer a large amount of data into and out of the AWS cloud. It’s device can hold up to 80 terabytes of data, and an AWS snowball edge device can hold up to 100 terabytes of data. It also provides data encryption. Schema conversion tool works both with snowball and snowball edge devices. 

Moreover, with AWS SCT and a snowball device, user can migrate their data in two stages. In the first stage, the user will use the AWS SCT tool to process the data locally and then move it to the snowball device. The user can send that device using the AWS snowball process. AWS automatically loads the data into an Amazon S3 bucket.  When data is available on S3, users can migrate their data to Redshift using the schema conversion tool.

Amazon Redshift Enterprise Data Warehouse Migration Tasks

Amazon Redshift

These steps enable the deployment of a pattern for a full data warehouse migration. Migration data warehouse to Amazon Redshift will help leverage more modern data architecture to deliver analytics on a broader range of data and toolsets. As reducing the cost and complexity associated with operating a traditional warehouse.

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Redshift is a fast, fully managed petabyte-scale data warehouse. Amazon  Redshift makes it easy and cost-effective to analyze all the data using existing business intelligence tools. The data warehouse brings together datasets from all across an organization into one place with Redshift to easily run queries process; Redshift natively supports distributed workloads. It incorporates a pretty neat feature called parameter groups so, an organization has different users that are all using this redshift cluster. Redshift is a great choice if a database is overloaded due to OLAP transactions, as mentioned earlier. Amazon Redshift is designed for OLAP, which allows to easily combine multiple complex queries to provide answers relational or sequel database are row-based.

It also supports petabytes of data, which is primarily controlled by adding additional nodes with a newer RAS series. It is also optimizing extensive queries that can take place over multiple different tables. Redshift will optimize the database for user queries. Amazon Redshift provides massive, parallel, shared-nothing columnar architecture.   

Why use Redshift for an Organization

Elastic Scaling: Redshift offers elastic scaling so the user can add or remove nodes to their cluster at any point.

Managed- Almost Zero Maintenance: Redshift is considered a managed service that sets up some alarms on sizing and CPU performance.

Optimized Query Performance: It also provides a very consistent and reliable performance for some frequently running queries.

Supports Thousands of Users with a Single Cluster: It can help thousands of users within a single cluster by scaling up the cluster and adding more nodes on top of it.

Flexible Pricing Model: If an organization uses on-demand, it is more expensive than the reserved instances, which need to purchase for a one-year commitment.

Compression

Goal: Amazon Redshift allows more data to be a store within an amazon Redshift cluster. It also reduces I/O for analytics queries and improves query performance by decreasing I/O.

Impact: Allows two to four times more data to be stored within the cluster.

Data Sorting

Goal: Make queries run faster by increasing the effectiveness of zone maps and reducing I/O.

Impact: Enables range-restricted scans to prune blocks by leveraging zone maps.

Columnar Storage

However, Redshift is a column-based database; the columnar data is stored sequentially on the storage so, it requires less read to get all the data, and it also allows to compress data. Columnar data can be pressed more comfortably as all the data types are the same because all the data is stored in one sequential row so, it’s much easier to compress than row storage. It only read the column data that is required without going through the whole data cluster.

Nodes

Amazon Redshift data warehouse is a collection of computing resources called nodes, and these nodes are organized into a group called a cluster. Each set runs an amazon redshift engine that contains one or more databases.

Limitations

The dynamic database limitation is that it is not highly available as it is only in one availability zone. The reason for this is that management business intelligence is not viewed as business-critical.

Workload Management (WLM)

Amazon Redshift allows for the separation of different query workload:

Amazon Redshift is significantly faster in a VPC compared to the EC2 classic.

Redshift Cost

Redshift cost charged for the number of compute node hours used, and that doesn’t include the lead-in through the leader node is not a chargeable node also charge for the backups stored by the users.   

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

 

Micro-Services vs. API

What are Microservices?

The term microservices is defined as its most commonly known as microservice architecture. It’s an architectural style to build the applications; these applications are software applications. So, users can structure an application to collect small or standard Services models around the business domain. In this article we are discussing Microservices vs. APII.

What is API?

API stands for Application Program Interface. API is a way users can make sure two or more applications can communicate to process the client request.

It’s a point of contact through which all the services communicate to process the client request and send the response. The client will send a particular request through API for the respective functionality or feature. That specific functionality or feature will retrieve the requested data and then send back the client’s response. So, API is working as a middle person between the client and application to communicate. When a client sends a request, it is called an HTTP request.  

Uses of APIs in Micro-Services

As we know, API works as a middle person between the client and the respective feature of the functionality. So, the particular feature or the functionality can go into a specific service. When the client requests all the products available in the application, the request will directly go to that particular service, and a response will generate back. To refactor an application into microservices, it will allow a specific service to add some particular functionality to it. Then it will provide its data access layer and database, and for each microservice, there is a separate API.

In a typical e-commerce application, customer microservice will have a separate API, data access layer, and database and similarly goes with the product micro-service and micro cart service. Microservices also allows using separate databases for every microservice or two or three microservices to share a common database. When a client sends a request to get information about a particular data, the API gateway will decide which service for this specific request has to be sent so that the client can retrieve the required data from the API. Then the API sends back the requested response to the client.

Difference between Micro-Service and API

Microservice and APIs are completely two different things. Microservices are an architectural style through which a user can build applications in the form of small autonomous services. API is a set of procedures and functions that allow the consumer to use the underlying service of an application.

Advantages of Microservice Architecture  

Independent Development: Each service can develop independently. If a user has a product service, they can update a service without redeploying the entire application bug fixes. Some new feature releases are more manageable and less risky because users need to focus on one particular service.

Fault Isolation: If a service goes down, it won’t take the entire application down with it, and it’s a great advantage.

Mixed Technology Stack: This feature allows teams to pick any technology that best fits their service.

Granular Scaling: With this feature, services can be scale independently.

Companies using Microservices

There are lots of large and small companies using microservices such as:

These are operating at a large scale because they become easier to manage when broken into smaller pieces. The use of micro-services with APIs is revolutionary to grow a business in less time. 

Users need some specific functionalities for their E-commerce application such as; Customer Information, products, online payments, product availability, etc., and microservice makes it easy for businesses. The combination of microservice and API allows the business application to communicate with each other, making a business more efficient and reliable. 

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Connect CTI Adopter for Salesforce

Amazon Connect CTI Adopter for Salesforce and Sales Cloud

Amazon Connect CTI supports the ability to take voice calls in a sales force agent experience and screen pop on the incoming phone number. Agents cloud also clicks to dial a number within their contacts. Amazon Connect CTI is Adopter’s new version adds several new features according to customers’ requirements, such as:

Single Sign-On: Single Sign-On provides seamless login with amazon connect and Salesforce.

IVR Data Tips: It allows users to inject Salesforce data into the customer’s experience easily; for example, businesses can offer personalized greetings and dynamic routing based on customer information.

Omni-channel: Omani-channel support allows businesses using Salesforce chat SMS and emails to ship presents with amazon connect. Amazon Connect will know when an agent is handling a Salesforce chat and make them unavailable for a voice call and vice versa. It allows the agents to manage all their voice calls and Salesforce digital channels.

Screen pop capabilities: Amazon Connect CTI Adopter brings improved screen pop capabilities. So businesses can pop the right screen within Salesforce based on any information they collected from the caller, such as phone number, case number, and account number.

Additionally, all the information collected can be shared with the agent before informing them of the call’s context before they even answer.

Case Management: all calls answered by the agent will be captured as an activity associated with the case. The agent can then go back to that activity and see the call information along with the associated voice recording.

Integrated Reporting:  This feature of Amazon Connect CTI Adopter allows agents and supervisors to view contact center dashboards from amazon connect.

Voice Transcription

Amazon connect also providing voice transcription of recorded calls and injecting the analysis into the case activity. Amazon will create transcriptions from user’s call recordings and then leverage amazon comprehends to determine sentiment analysis and push in the agent’s Salesforce for viewing.

Silicon Valley Cloud IT provides these exciting customer service experiences built with Amazon Connect and Salesforce service cloud.

CTI Data Connector for Salesforce

CTI data connector is packed with features that provide all the benefits of using Skype for business with Salesforce; for example, a pop-up shows the caller details when a call comes. Moreover, users can automatically create an activity for each call with additional information like phone notes call results. Call duration automates workflows like creating a case or document and log missed calls. All history gives a brief overview of in and outbound calls, missed calls, and the quick action link to edit the phone note.

CTI data connector for Salesforce is flexible to fit customers’ needs with Skype for business connecting to an on-premise PBX or cloud PBX service.  

Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.
        

Amazon Web Services vs. Google Cloud Platform

The demand for a public cloud solution is rising at an extensive rate. Amazon cloud services and Google cloud platform playing their significant part; here is a brief comparison of both services.

Amazon web services is a subsidiary of amazon cloud computing platforms to individuals, companies, and governments on a paid subscription basis.

Similarly, Google provides a suite of cloud computing services that run on the same infrastructure that Google uses internally for its end-user products, such as Google search engine and YouTube.

Amazon web services are useful to create and deploy any application in the cloud. It provides services over the internet. It was launched in the year 2006.

Google Cloud Platform is a cloud computing service that offers application development and integration services for its end users. It was launched in the year 2008.

Establishment of Amazon web services and Google Cloud Platform

Aws launched in 2006, thus having more experience in the cloud domain than google or other cloud computing providers especially because it was established based on Amazon’s real-life experience and business needs. It successfully meets the on-demand needs of enterprises regarding the cloud computing platform.

Google Cloud was launched in 2008, which is no doubt the third competitor after azure and is quite a reliable and cheap cloud computing platform for businesses.

How is Amazon web services and Google Cloud Different?

Here we are comparing AWS vs. GCP. Some important factors need to focus on while comparing AWS and Google cloud, such as:

Services

AWS covers services such as; Amazon web services EC2, RDS, S3, IAM, VPC, Cloud watch, Cloud 9, IOT core, etc. Similarly, Google cloud covers cloud engines, cloud data store, cloud storage, cloud IAM, cloud DNS cloud SDK, Cloud IoT, etc. Google cloud services are lesser in number than AWS.

Both Amazon web services and Google Cloud can integrate with multiple open source tools links Docker, Ansible, GitHub, Jenkins, Kubernetes, tensor flows, etc.

Virtual Servers – AWS EC2

It is a web service that helps to resize and compute capacity to run application programs on a virtual machine.

Virtual Servers – GCP VM Instances

Google Cloud Platform enables a  user to build, deploy, and manage virtual machines (VMs) to run the cloud’s workloads.

AWS EC2 Pricing

Amazon web services provide a free tier for the first 12 months in EC2 service (up to 750 hours per month).

Google Cloud Platform (GCP) VM Instances Pricing

It offers a free tier that includes a micro instance per month for up to 12 months.

PaaS – AWS Elastic Beanstalk

It is a Platform as Service (PaaS). It is an orchestration service for deploying applications and maintaining capacity provisioning, load balancing, auto-scaling, and application health monitoring.

PaaS – Google App Engine

Google App Engine is a service used by developers to build and host applications in Google’s data centers.

VPS – Amazon Light Sail

It is useful for a web application that requires a minimum number of configurations.

VPS – Google Cloud Platform (GCP)

 Google Cloud Platform doesn’t have any VPS service.

Server-less Computing – AWS Lambda

It is a server-less compute service. It allows the execution of backend code and scales program data automatically when required.

Server-less Computing – GCP Cloud Function

It is the easier way to run code in the cloud; also, it is highly available and fault-tolerant.

Disaster Recovery – AWS Disaster Recovery Services

It is a cloud-based recovery service that helps in the last recovery of data resulting in minimal downtime.

Disaster Recovery – GCP

Google Cloud Platform doesn’t have any disaster recovery service.

Downtime and Speed

Another factor is downtime and speed; AWS’s maximum downtime phase in 2015 was 2 hours and 30 minutes, whereas having a progressing infrastructure. Google faced a massive downtime of 11 hours and 34 minutes in 2015, and that is why when we compare the reliability in both, this is where AWS comes out to be a preferable choice.

Google Cloud Platform has a complex pricing schema than AWS. Downloading data from GCP is expensive as compared with Amazon web services.

AWS is less expensive than GCP and provides enterprise-friendly services. It is also outstanding in speed and agility and offers secure and reliable services which have attracted more enterprises to adopt AWS services to grow their businesses.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Business teams have to manage their work, data to report, and goals to crush on a daily basesbasis. But whether it’s managing a project or tracking customers, the traditional general enterprise software that was not built for your business needs can cause issues to manage specific business needs. For example, many people using spreadsheets to manage their data, but spreadsheets are not designed to adapt to a business. Spreadsheets are not great for syncing data across the team. Sometimes people use project management tools, but they are hardly customizable as everyday business changes. AWS Honeycode provides applications that are designed specifically for your team’s business needs. AWS Honeycode technology gives you the power to create a specific business application according to business needs. With AWS Honeycode, a user can quickly build custom business apps that help a business team to manage their work without programming.  It is a fully managed service that quickly builds powerful mobile and web applications without coding. With AWS Honeycode, users can use a spreadsheet interface along with a visual application builder to create basic apps.  Dynamic Features with Privacy A user could run a budget approval process where approvers review requests made through an app. When requests have been approved, a team can see the live updates in the application, and each person only sees data they are authorized to see. AWS Honeycode allows building an app for personal use and business teams with a visual editor.  Building Process to Build an Application with AWS HoneyCode Set up your business data in a table to manage the database. •	Build the app’s layout •	Add content •	Link data from the tables •	Set personalization, so each user sees data they need to see.  •	Set automated actions that run whenever conditions are met, such as when data is updated  •	And share the app with your team so everyone can work together  How AWS HoneyCode makes you a Smart Businessman AWS HoneyCode allows us to build applications for scenarios such as:  •	Customer Tracking •	Resource Tracking •	Operations Monitoring •	Approval Processes •	Project Management Launch a Cost-Effective Application with AWS HoneyCode With Amazon, Honeycode, users can get started creating applications in minutes. It allows building applications with up to 20 users for free. The user only pays for the number of users and amount of data used for larger applications. It also reduces the developer’s cost with its no coding interface; even a non-technical person can build an application according to their needs.  Limitations for Security It allows its users to add a lot of intelligence to spreadsheets.  It also allows setting limitations for logging access, data access, etc. Customaization  Users need to update their application, and the updated data is shared across the team members automatically. Users can use a spreadsheet interface along with a visual application builder to create applications. Users can build their applications for scenarios such as: •	Customers Tracing •	Resource Tracking •	Operations Monitoring •	Approval Processes   •	Project Management  	 	     Responsive AWS HoneyCode provides highly responsive and compatible applications for different screen sizes such as laptops, mobile view, and large screens, etc., with a user-friendly interface.   Automate Manual Setup AWS HoneyCode helps automate manual steps such as a user can define rules, and based on the prescribed rules, events will happen. Author: SVCIT Editorial Copyright Silicon Valley Cloud IT, LLC.

Business teams have to manage their work, data to report, and goals to crush on a daily basis. But whether it’s managing a project or tracking customers, the traditional general enterprise software that was not built for your business needs can cause issues to manage specific business needs. For example, many people using spreadsheets to manage their data, but spreadsheets are not designed to adapt to a business. Spreadsheets are not great for syncing data across the team. Sometimes people use project management tools, but they are hardly customizable as everyday business changes.

Amazon Honeycode provides applications that are designed specifically for your team’s business needs. AWS Honeycode technology gives you the power to create a specific business application according to business needs. With AWS Honeycode, a user can quickly build custom business apps that help a business team to manage their work without programming.  It is a fully managed service that quickly builds powerful mobile and web applications without coding. With AWS Honeycode, users can use a spreadsheet interface along with a visual application builder to create basic apps.

Dynamic Features with Privacy

A user could run a budget approval process where approvers review requests made through an app. When requests have been approved, a team can see the live updates in the application, and each person only sees data they are authorized to see. AWS Honeycode allows building an app for personal use and business teams with a visual editor.

Building Process to Build an Application with Amazon HoneyCode

Set up your business data in a table to manage the database.

How Amazon HoneyCode makes you a Smart Businessman

Amazon HoneyCode allows us to build applications for scenarios such as:

Launch a Cost-Effective Application with Amazon HoneyCode

With Amazon, Honeycode, users can get started creating applications in minutes. It also allows building applications with up to 20 users for free. The user only pays for the number of users and amount of data used for larger applications. It also reduces the developer’s cost with its no coding interface; even a non-technical person can build an application according to their needs.

Limitations for Security

It allows its users to add a lot of intelligence to spreadsheets.

It also allows setting limitations for logging access, data access, etc.

Customization

Users need to update their application, and the updated data is shared across the team members automatically. The users can use a spreadsheet interface along with a visual application builder to create applications. Users can build their applications for scenarios such as:

Responsive

AWS HoneyCode provides highly responsive and compatible applications for different screen sizes such as laptops, mobile view, and large screens, etc., with a user-friendly interface. 

Automate Manual Setup

AWS HoneyCode helps automate manual steps such as a user can define rules, and based on the prescribed rules, events will happen.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Lambda Serverless Function

AWS Lambda is one of the services that fall under the ‘Compute’ domain of services that AWS provides along with Amazon EC2, Amazon EBS, and Amazon Elastic Load Balancing. It allows us to compute code or any application with AWS Lambda. Users can run code for virtually any type of application or backend service. AWS Lambda automatically enables runs code without requiring manual provision or management servers. Users have to write code in one of the languages that the AWS Lambda function supports and upload it to Lambda, and Lambda will take care of it. AWS Lambda supports node.js, java, C sharp, Go, and Python.

Where is Lambda used in Different Businesses?

There are some ways that businesses use AWS Lambda, such as:

How does Lambda Work?

Clients send data to the lambda function, and Lambda receives the request, and the data will run on the defined number of containers. The number of containers depends on the volume of data. So, if it is a single request or less request, it runs on a single container. The requests are given to a container to handle. A container contains the code the user has provided to satisfy the query. Moreover, with AWS lambda no need to install software, for example, Webserver and App server in the underlying environment to run a process. However, code libraries can be installed.

AWS Lambda Environment

Underlying infrastructure managed by the provider:

Cannot install the software (e.g., Webserver, App Server) in the underlying environment:

Easy selection of computing power:

No attached hard disk, but the deployment package is size limited 

Serverless Use Case

Suited at driven architectures

Microservices

Shines at event architectures

Why AWS Lambda?

 Some of these benefits that are offered by AWS Lambda such as:

Scalability: Amazon doesn’t have any servers to provision or to manage, so that means it provides a lot of leverage in scaling lambda functions when the requests grow. Amazon uses containers according to the number of requests. Users do not need to worry about scaling their application or setting up for auto-scaling configuration. Because the scalability part and availability of that environment are taken care of by Amazon and users, they need to focus on their application code and making their customer experience better.

Reduces Servers’ Cost: Lambda reduces the cost of servers.

Automatic Scaling: Lambda also automatically scales applications by a running code in response to each trigger.  Code runs in parallel and processes each triggers individually, scaling precisely with the size of the workload. Lambda can scale the application running the code in response to each trigger that it receives.

Metering on the Second: Users only pay the amount of time, which means users don’t need to charge for servers.  The only payment required is for the amount of time the code is computed. With AWS Lambda, users will charge for every hundred milliseconds while code executes and the number of times their code is triggered. 


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Salesforce Platform for Business

Salesforce Platform for Business

In this digital age, business is changing fast, and everything is connected through digital mediums. Salesforce makes it easy to use cloud-based business applications to stay connected to customers, prospects, and more. It is one of the world’s best CRM platforms, enabling a business to sell, service, and market like never before.  Salesforce is a customer success platform, helping organizations connect to their customers in a whole new way. It is also known as the next generation of spreadsheets.   

How Businesses Offer Great Services using Salesforce Platform

An organization can grow its business much faster, succeeding at every step without all the hiccups. Even without having a big call center or multiple service agents on hand, small companies are more than capable of offering excellent customer service with the salesforce platform.

Salesforce Platform also providing three key strategies for a small business that allows users to lay the foundation for excellent customer service, outstanding customer experiences, and lasting customer relationships.  

Meet Customers Where They Are

Moreover, the Salesforce platform allows its users to connect with their customers by using phone, over emails, social media, or on business listening sites. A small business needs to know which channels customers love to use to reach them quickly and easily.

Helping Customers Help Themselves

A simple online help guide or FAQ page can answer the most commonly asked questions that small business customers have.  It saves time for the customer; it also saves time for the business.

Have Context on the Customers

Salesforce platform solutions provides this cool strategy to know who your customers are and what is essential to them, make a support interaction, and a great overall experience. A personal touch often makes folks want to do business with a small company; they want relationships, not just for transactions.       

The Salesforce platform is cloud-based, it keeps all your information up-to-date in real-time, and a company can access their data and information from anywhere at any time. They can also run their entire business from their phone. Salesforce is providing the world’s leading enterprise cloud ecosystem.

Some key features of the Salesforce platform are as follows:

Sales Cloud

With sales cloud, an organization can save their information they need to close deals, collaborate, and sell as a team. It also allows to manage contacts and track opportunities.

Service Cloud

Service Cloud allows you to deliver a world-class customer service experience.

Marketing Cloud

With a marketing cloud platform, an organization can create personalized one-to-one customer journeys and powerful multichannel marketing campaigns that generate leads and drive sales.

Community Cloud

With the community cloud, users can build vibrant, engaging communities that help customers, partners, and employees help themselves and each other.

Analytics Cloud

It helps a business to make quicker, smarter decisions with the analytics cloud. Turn big data into a significant advantage by uncovering new insights and taking action instantly from the device.

App Cloud

App Cloud users can build modern employee and customer-facing apps that engage and excite all within a secure, trusted, and instantly mobile environment.

IoT Cloud

With IoT cloud, users can connect all their data from the internet of things to the rest of the Salesforce Platform for better insights and real-time customer actions.

Salesforce Administration

Salesforce Platform administration manages and administers production organization and other users.  It provides the facility to a business to ensure the releases of products deployed to the production are on time, and the environment is stable after release. It also makes sure that the user profile and the licenses comply as per the needs. Salesforce administration is also responsible for keeping track of the project’s progress and ensuring that there is no gap between the requirement specification and the actual project development.

Salesforce APIs

API stands for application programming interface; it is nothing but an interface with the help of which two systems can communicate together. Salesforce takes an API first approach to building features. API is an extremely efficient way for us to develop features in business applications. The key thing is that APIs enable communication by providing a standard interface for web applications to communicate with each other. Applications that are not API-enabled are like closed doors in cyberspace.

Salesforce APIs can give a business power to connect with a wide variety of applications such as ERP, HR, homegrown, legacy systems, and financial applications. Each of these applications exchanges data with salesforce. Salesforce allows building dashboards and reports. By using Salesforce APIs, users can automatically create tasks, send emails, update fields, and schedule these actions to occur on the required time.     

Salesforce has got a wide variety of APIs such as data API which is used to interact data on the platform. Streaming API work for real-time updates of data; chatter API, analytics, metadata, and tooling API. Salesforce Platform also allows us to create a custom API.  


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

What is Amazon Connect Center

Every business needs to delight its customers with a personalized experience. Creating a delightful experience with traditional on-premises contact centers is difficult because they are expensive to maintain, and it can take months to make even the simplest of changes.

Amazon builds its contact center allowing its users to serve their customers. It scaled it to support millions of Amazon customers and over a billion unique customer interactions.

Amazon Connect

Amazon Connect is an easy-to-use Omni-channel cloud contact center that enables us to seamlessly deliver a dynamic, natural, and personalized experience that enhances customer service. Every customer interaction created by a user is immediately available across voice and chat channels without any duplication of effort. It saves time and money; an organization can quickly introduce their Chatbot for their customers using the same natural language understanding technology that powers Alexa. Users do not have to be an engineer to do it. It helps businesses of any size by delivering better customer service at a lower cost. Contact centers hold valuable insights related to brand perception and customer satisfaction of a company.      

Amazon Connect Contact 

Amazon Connect center can predict customers’ needs by analyzing customer searches; every conversation across every channel can be analyzed by AWS machine learning and artificial intelligence functionality. Allowing a business organization to understand customer conversations without manually auditing every contact with low pay-as-you-go pricing users can save up to 80% over other contact centers.

Amazon Connect Differentiators  

Amazon Connect increasing the flexibility for work-at-home agents. Beyond the standard functionality, amazon connect delivers several differentiators that allow AWS customers to create exceptional customer experiences. Amazon Connect is a cloud-based system; there are no adapters, applets, applications, or browser extensions to install. It can be set up in minutes, and agents can take calls after just a few simple steps and can be located practically anywhere with a broadband internet connection.

 Self-Service Configuration

The self-service graphical interface in amazon connects easily for non-technical users to design contact flows without special skills. The contact flow engine is dynamic and personal users can design conversational interactions that feel natural to customers by integrating with Amazon Lex. It gives access to the same speech recognition and natural language understanding technology that power Alexa. 

Amazon Connect is an open platform that is simple to integrate with other enterprise applications; by integrating with customer data, users can anticipate end customer needs predicting and delivering answers to questions before they are even asked.

Challenges faced by Contact Centers

Contact Lens for Amazon Connect

Contact lens addresses all these challenges and provides a set of new machine learning-based analytics capabilities. In addition, these new analytics capabilities are part of an out-of-the-box experience Amazon connects contact lens build to ensure that contact center users such as supervisors and Q/A analysts can use machine learning-based features with just a few clicks. There is no technical expertise required to use contact lenses. Like amazon, connect contact lenses require no contract or upfront commitments, and users pay for their use. 

Core Features of Amazon Connect Lens

With amazon connect, users can easily configure, which calls users to analyze the amazon connect contact flow by selecting the “contact lens speech analytics checkbox” feature in the set recording and analytics behavior block. Once the user has completed, the configuration contact lens will automatically start analyzing calls that pass through this contact flow block.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Is No-Code scalable enough for enterprise

No-Code bridges the gap between user-friendly platforms and customization for enterprise solutions. It is scalable enough for enterprise software solutions.

No-Code platform allows linking a front end, backend, and database of a full-stack application without paying a tremendous amount for development and maintainability.  

No-Code Platform Scalability for Enterprises

If an enterprise’s application is not built to be scalable, an organization will face a significant downfall down the software lifecycle that can cost them millions of dollars. Making a scalable application has everything to do with structure and strategy because these two aspects are fundamental to run a scalable enterprise application.  If an enterprise application is not scalable, then a business will face problems in some areas such as:

Add new Features: As scalable applications help a business to grow their user base, functionalities, and feature sets with perfect performance. If an enterprise application is not scalable, a company will face some problems when adding new features or expanding the application with extra features.

Testing: Testing is a significant part of providing a final application with all required functionalities for a business. Moreover, its Smooth and scalable testing helps to test an application under all enterprise requirements for a business. No-Code provides a smooth testing functionality for features because traditional custom applications give issues during testing. Hence, No-Code platform application scalability also provides better testability.

Performance: The overall performance of an application is also a significant factor in software scalability. A business wants the application to perform well, fast in speed, give their users a good experience, and all this possible using No-Code. No-Code can resolve the issue that every enterprise have to face with custom development such as:

No-Code core advantages for Scalable applications

No-Code scalability for enterprise

No-Code platform combines the simplicity of an enterprise solution with the scale of DevOps automation. Customers and industry experts have come to recognize the No-Code platform as the most open and modern application delivery platform. It allows an organization to deliver cross-platform, hybrid mobile apps that are native in every way, with access to device features and offline data. It provides ready-to-go applications with one-click testing and development to release pipeline in the cloud or on-premise infrastructure.

It also allows the integration of existing services like:

No-Code scalability empowers business users to assemble a solution rapidly with a wide range of functionalities. An organization can add more features and more functionalities according to their needs.  

An enterprise spent a lot of money to build their enterprise software for their customers to use their software and interact with them. Still, as a business grows, new requirements will start piling up faster than an organization can process them by handling a massive amount of data with spreadsheets, causing nasty errors and crashes. At this point, now it’s time to break the traditional boundaries and go with No-Code. With this platform, an enterprise can manage its business processes with applications that used to be too complicated to build without professional help. 


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

5G Technology for Business Solutions

Every new generation of wireless networks delivers faster speed and more functionalities to our smartphones. 1G brought us the first cell phones, 2G let its users text for the first time, and 3G brought us online, and 4G delivered the speeds that users enjoyed today. But as more users come online 4G networks have just about reached the limit of what they are capable of when users want even more data for their smartphones and devices.

4G fourth generation replaced 3G. 4G spec means its specification that defines minimum requirements, type of technologies, frequencies, uses, and standards. The maximum speed of a 4G connection might be 150 megabits per second, but it’s not likely users get that.

What is 5G Technology?

The world is a mass rollout of 5G Technology networks, while the deployment of 5G requires financial investment. It will make business faster and cheaper. 5G network technology is the next generation of wireless. It can handle a thousand times more traffic than today’s networks and is ten times faster than 4G LTE. 5G Technology will be the foundation for virtual reality (VR), autonomous driving, and the internet of things (IoT). 5G Technology is 100 times faster than the current 4G, and the maximum download throughput can offer 10-20 Gbps, which means a user can download 2-3 HD DVD movies just in 1 second.

 What makes 5G so exciting?

Five brand new technologies are emerging as a foundation of 5G, such as:

Business Benefits of 5G

There is some attribute required for a network to be genuinely 5G, which will empower businesses to compete in this era.

Throughput: It provides greater throughput and faster speed than other wireless technologies.

 Service Deployment:  Update for software instead of hardware so that the network can stay secure and responsive to businesses.

Mobility: The reliable ability to connect devices in motion even at high speed.

Connected Devices: 5G is a network with the potential to support up to one million connected devices. 

Increased Speed and Bandwidth: 5G Technology will bring speed 10 to 100 times faster than 4G. Further, More bandwidth for WAN connections means more potentials for office automation and perhaps downward pressure for WAN connectivity prices.

Low Latency: 5G’s low latency, as low as one millisecond, will give businesses the flexibility to replace expensive MPLS infrastructure for the line of business applications. Moreover, It has the potential to reduce the data sending response time and making applications more responsive.

Devices density: 5G will enable up to 100 times more connected devices in one given area than 4G, which means a more significant potential mobile customer base.

Reduce Power Consumption: It reduces energy consumption with edge computing. 5G will have lower power overhead in design and consumption at the infrastructure level, which will stretch the life of remote IoT devices.

Security: 5G will bring security for designers, including hardware security modules and key management services, to ensure that data sent over 5G networks are secure.

Data Volume: 5G allows the facility to deliver a more massive amount of data to more users simultaneously with minor time delay and without losing data quality.

Reliability: A  complete 5G network solution that reliably delivers the 5G performance for the business domain.

5G is expected to become mainstream over the next five years, which these benefits will become apparent to businesses. The core investments of 5G Technology are Fiber, Millimeter-Wave Spectrum, Small Cell deployment, and edge computing, which will provide a 5G ultra-wideband network ready for the fourth industrial revolution. In short, 5G is a go-to solution for a business.   


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.
 

Microsoft Azure Cloud for Business

In this modern era, customers have been traditionally adopting the cloud by just moving their virtual machine of states up into the cloud which is a relatively easy well-trodden path but doesn’t necessarily realize the full potential of cloud benefits. So, it does not allow to minimize cost, does not allow to take advantage of scalability or performance capabilities.

How is Cloud Adoption Changing?    

Customers on the cutting edge of cloud adoption tend to be building out their application and estate to utilize the benefits of cloud platform services to a greater extent and be able to build applications to be more flexible and scalable rather than fixed size for a long period.

Microsoft Azure Cloud is one of the fastest-growing and the second-largest cloud computing platform in the market right now. It’s an online portal through which you can access and manage resources and services. It is free and also provides a pay-per-use model. It has its data centers all across the world.

Microsoft Azure Cloud Categories

Azure services have 18 categories and more than 200 services. Some of its categories’ names are as follows:

Microsoft Azure Cloud Service

Compute Service–Virtual Machine: Create Windows or Linux virtual machines of any configuration in a matter of seconds.

Cloud Service: Users can create scalable applications within the cloud using virtual machine provisioning, loading balancing, and health monitoring which is handled by Azure post-deployment.

Service Fabric: Service Fabric simplifies micro-service development and application lifecycle management.

Functions: The user can quickly build applications using server-less functions in any programming language of the user’s choice.

Networking-Azure CDN: Azure CDN services are useful for delivering high bandwidth content to users worldwide.

Networking-Express Rout: Express route lets users on permission networks into Microsoft Cloud through a private connection.

Networking-Virtual Network: Virtual Network enables Azure resources to communicate with each other securely.

Networking- Azure DNS: Azure DNS is a hosting service allowing users to host their DNS and system domains in Azure. Users can host their applications easily using Azure DNS.

Storage-Disk Storage:  Disk storage provides cost-effective HDD/SSD options which can be useful with the Azure Virtual Machine.

Network-Blob Storage: Blob storage is optimized for storing a massive amount of unstructured data, such as text or binary data.

Increase Business Agility with Azure

Microsoft Azure cloud is fast across the board. Here are some advantages of Microsoft Azure are as follows:

Why Microsoft Azure Cloud for Business

Four reasons small and medium businesses should move to the Microsoft Azure Cloud:

Cost-Effective:

No Downtime:

At Your Own Pace:

Unmatched Security:


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Spring Boot Framework

Spring Boot Framework is an enterprise Java framework that lets its users write enterprise Java applications. By using Spring Boot, users can bootstrap or quickly start up a simple Spring application. Developers can build complex applications with Spring Boot quickly. It’s a Spring module that aims to simplify using the Spring Boot Framework for Java development.

Moreover, Spring Boot Framework provides the feature of working POJOs and provides dependency injections, AOP, MVC, Security, Batch, and Data. We can say that the Spring Boot framework is where users can achieve any business purpose, and the best part of the Spring Framework can integrate with other frameworks, such as Hibernate and Struts. Spring Boot framework gives a production-ready application, so; there is no need to do any configuration.

Characteristics of Spring Boot Framework

Features of Spring Boot Framework

The features of Spring Boot are as follows:

Spring Boot CLI: The Spring Boot CLI allows us to Groovy for writing Spring Boot applications and avoids boilerplate code.

Started Dependency: This feature helps; the Spring Boot aggregates common dependencies together and eventually improves productivity.

Spring Initializer: This is a web application that can create an internal project structure for users.

Auto-Configuration: The auto-configuration feature of Spring Boot helps in loading the default configurations according to the project.

Spring Actuator: This feature provides help while running Spring Boot applications.

Logging and Security: This feature of Spring Boot ensures that all the applications made by using Spring Boot are secured adequately without any hassle.

Why Need Spring Boot?

Spring vs. Spring Boot

As both sound the same name Spring and Spring Boot. These terms are often one of the most confusing terms, but there is little difference between them. 

Spring Spring Boot
It takes time to have the spring application up and running A shorter way to run the spring boot application
Manages the life cycle of Java No need to worry about manually configuring a data source
Dependency Injection Framework A Pre-Configured set of framework/technologies
Web application Framework based on Java It is a module of Spring and is used to create a Spring application project which the user can just run or execute
Spring provides tools and libraries to create customized web applications It takes an opinionated view of the platform
More complicated than a spring boot Spring Boot is less complicated than the Spring Framework

Advantages of Spring Boot

For the installation and setup of Spring Boot, there are two ways such as:

  1. Spring Boot CLI
  2. Spring Tool Suite (STS)

System Requirements for Spring Boot Framework

Spring Boot 2.1.7 Release requires

Explicit Build Support

Servlet Container Support


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Auto Scaling and benefits

For enterprises, a business organization has to spend a lot of money purchasing the infrastructure to set up some solution that requires a one-time cost. However, it is a burden for a business organization to procure server hardware software and then have a team of experts to manage all those infrastructures.

Every business needs a cost-efficient solution for their projects; AWS Auto-Scaling magically maintains the application performance based on the user requirements. At the lowest possible price, AWS, Auto-Scaling also manages the scalability with optimized cost.

AWS Cloud Scaling

Aws Auto-Scaling is a service that helps the user to monitor their applications and automatically adjusts the capacity to maintain steady, predictable performance at the lowest possible cost.

Benefits of Aws Auto-Scaling

Better Fault Tolerance: It gave better fault tolerance applications. Users can get the servers created and can have a blown copy of the servers. It also saves time and effort to deploy the applications again and again.

Better Cost Management: Aws Auto-Scaling provides better cost management because Aws’ scalability is automatically pre-scheduled on some threshold parameters. 

Reliable Service: It is a reliable service, and whenever the scaling creates or initiate, users can get the notifications onto their mail IDs or their cell phones.   

Scalability: Scalability is a core feature of Auto-Scaling; it can be scaled up or scaled down.

Flexibility: It has flexibility in terms of whenever users want to schedule this service, stop this service, or keep the size of the servers at a fixed number.

Availability:  Auto Scaling service also provides better availability.

Snapshots Vs. AMI

Moreover, With Auto-Scaling, users can install multiple virtual machines in lesser time by using AWS Snapshots or AMI services.

Snapshots AMI
It is useful as a backup of a single EBS volume, just like a virtual hard drive attached to the EC2 instance. AMI is useful as a backup of an EC2 instance.
The snapshots opt for this when the instance contains multiple static EBS volumes. AMI allows users to replace a field EC2 instance.
Pay only for the storage of the modified data Pay only for the storage that you use
It is a non-bootable image on EBS volume It is a bootable image of the EC2 instance

How Does Aws Auto-Scaling Work?

To further, the Aws Auto-Scaling, users have to configure a single unified scaling policy application resource. With that scaling policy, the user can explore the applications and select the service for scaling. Auto-scaling also provides two types of optimization: cost optimization and performance optimization to choose any optimization according to their needs. They can also keep track of scaling by monitoring or getting notifications.

Different Scaling Plans

Types of Scaling

Scaling consists of 2 types:

Dynamic Scaling: It guides the service of Aws Auto-Scaling on how to optimize resources. It is also helpful in optimizing resources for availability and price. With this scaling strategy, users can create their plans based on the required metrics and thresholds.

Predictive Scaling: Its objective is to predict future workload based on daily and weekly trends and regularly forecast future network traffic. It uses machine learning to analyze network traffic. This scaling is like how weather forecast work. Moreover, it also provides scheduled scaling actions to ensure that the resource capacity is available for application requirements.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Low-code Development Revolution for Business

In the fast-paced business world, generic solutions are used to create a lot of overhead in terms of time and money; don’t let this technical debt stall business growth.

However, It’s time to get rid of the one-size-fits-all approach and build custom solutions that drive the business’s digital transformation. For creating custom applications, a user needs to rethink the process involved.

Traditional application development may cause long-term headaches, but at what cost are organizations seeking to reduce delivery time, improve resource utilization, and increase productivity by implementing a new Low-Code application for the enterprise? As the name suggests, Low-Code development requires drastically fewer lines of code to produce the same result as a traditional programming language. Low-Code helps us to design custom workflows on an already abstracted layer without thinking about the underlying complexity. 

Does Low-Code Development deliver value to your business?

Low-Code offers an interface to bring solutions to the market faster than the competition. Moreover, it can deploy hundreds of secure and scalable enterprise-grade applications on a platform. It tightens governance, eliminates high-paid coders’ demand, and makes changing apps painless with its built-in data schema.

Thus, Low-Code development for enterprise applications lowers the barriers created by traditional development and bridges the gap between IT and business teams.

Now developers can spend less time on repetitive work and focus more on solving a specific business problem while many organizations are stuck on where to begin their transformation efforts.

Here are some significant benefits of Low-Code development for an enterprise business are:

Low-code development provides fully enterprise-ready integration scalability, security, and fast integration with effective ERP solutions as businesses are looking to save high costs for process management. So, the significant market opportunity for a largescale of enterprise is adopting Low-Code. It is a cost-saving solution compared to traditional coding and offers considerable advantages to software development houses.

Low-Code Business Process Management Tools

Low-Code business process management tools implemented on a single intelligent platform to accelerate the processes of the business. Moreover, the Low-Code development platform allows us to manage unstructured, dynamic processes. Designing a new case, a user rarely has to design a strategy from scratch. It provides business logic with smart AI-based capabilities, and its architecture and package mechanism enable continuous integration and continuous delivery of changes and updates natively.

Although, Low-Code solutions are highly scalable and demonstrate high performance across multiple large-scale businesses with tens of thousands of active users. Low-Code enables everyone fundamentally at its core. The Low-Code says that there is no need to have professional developers; it allows a non-technical person to develop their idea without delaying control of their destiny. It is complete democratization of application development for indeed the first time at a broad worldwide scale. So, an organization can fulfill the digital need of their business by using a Low-Code development platform.

The emergence of Low-Code in a Cloud-Native Way

Top Low-Code Development Platforms

Salesforce: Salesforce platforms automating repetitive processes; it supports austere object access protocol environments as well as representative state transfer protocols. 

Kissflow: Kissflow automates workflow and up the productivity of an organization. The Kissflow environment is closely coupled with the Google Apps portfolio. It can also use to collect information from dynamic tables and environments. 

Outsystems: Outsystem delivers a variety of robust features, including third-party libraries and application templates and the proprietary integrated development environment.

Quick Base:  It is used to build applications to support the collection and storage of information. Quick Base uses pre-built application connectors to support integration with popular cloud-based solutions while supporting advanced security features.

Radzen: Radzen helps customers build applications and access services with ease as order files are available to open in visual studio code.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Introduction to Apache Kafka

Apache Kafka is an open-source publish/subscribe messaging system. It is a distributed event log where all the new records are immutable and appended to the log’s end. In Kafka, messages may persist on disk for a specific period known as the retention policy; this is usually the main difference between Apache Kafka and other messaging systems and makes Kafka in some way a hybrid between a messaging system and a database.

Apache Kafka’s central concept is producers producing messages on different topics and consumers consuming those messages and maintaining the data stream. A user can think about producers as publishers or senders of messages. On the other hand, consumers are analogous to the receivers or subscribers.

Why Apache Kafka

Apache Kafka aims to provide a reliable and high throughput platform for handling real-time data streams and building data pipelines. It also provides a single place to store and distribute events that can accumulate into multiple downstream systems, fighting the ever-growing integration complexity problem. Kafka is popular to build a modern and scalable ETL (Extract Transform Load) change data capture or big ingest systems.

It is useful across multiple industries, from companies like Twitter and Netflix to Goldman Sachs and Paypal.

Kafka Architecture

Apache Kafka architecture is consists of a high-level Kafka cluster, producers, and consumers. A single Kafka cluster is also famous as a broker. Its cluster usually consists of at least three brokers to provide redundancy. These brokers are responsible for receiving messages from producers, assigning offsets, and committing messages to the disk. It is also responsible for responding to consumers’ fetch requests and serving messages. In Kafka, when messages deliver to abroker, they are sent to a particular topic. 

Topics: Topics provide a way of categorizing data that is delivered, and they can further break down into several partitions; for example, a system can have separate topics for processing new users, and for processing metrics, each partition as a separate commit log and the order of messages is guaranteed only across the same partition.

Splitting a topic into multiple partitions makes scaling easy as a separate consumer can read each partition. For achieving high throughput as both partitions, and consumers can split across multiple servers.

Cluster

Producers are usually other applications producing data; this can be, for example, our application producing metrics and sending them to our Kafka cluster; similarly, consumers are generally other applications consuming Kafka data.

Kafka often acts as a central hub for all events in the system, which means it is a perfect place to connect. If a user is interested in a particular type of data, a good example would be a database that can consume and persist messages or an elastic search cluster that can consume certain events and provide full-text search capabilities for other applications.

Messages in Kafka and Data Model

In Apache Kafka messages, a single unit of data can send or received as far as Kafka is concerned; a message is just a byte array. A message consists of an optional key and byte array to write data in a more controlled way to multiple partitions within the same topic.

The Apache Kafka data model consists of messages and topics.

Kafka Use Case

Apache Kafka is useful for various purposes in a business organization, such as:

Messaging Service:  Kafka messaging service allows us to send and receive millions of messages in real-time.

Real-Time Stream Processing: Kafka can process a continuous stream of information in real-time and pass it to stream processing systems such as Strom.

Log aggregation: Kafka can collect physical log files from multiple systems and store them in a central location such as HDFS.

Commit Log Service: Kafka is useful as an external commit log for a distributed system.

Event Source: A time-ordered sequence of events can maintain through Kafka.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

No-Code Platforms

Today, the world runs on code. Every text you send, every website you visit, every screen you swipe it’s all driven by code. Instead of writing hundreds of lines of code to add new features to a software application, a business management cloud design them on their business application. No-Code platforms allow the creation of the next world-changing products without writing a single line of code.   

Advantages of No-Code Platforms

No-Code application development processes provide a visual development approach to rapid application development. Neither requires experience with a traditional programming language to create applications. They are making app development accessible to a more significant number of people, specifically tech-savvy individuals working in business lines.

Easy Integration for Infrastructure

No-Code strips away the overhead of standing up environments and maintaining the infrastructure by them always offered in a platform-as-a-service or PaaS from factor. However, that’s pretty much where the resemblance ends. Most No-Code application development tools tend to be data first, departmental productivity, enhancement solutions like forms-based data input, simple reporting, dashboard, and lightweight back-office automation.

No-Code Reduces Development and Integration Cost

No-Code platforms enable less technical business users to build solutions fast because they are designed to solve functional use cases. This platform does not require application developers to make considerations for architecture. It also restricts integration to vendor-provider solutions.

While the ease, simplicity, and rigidity of the backend into pure No-Code development work at the development level, scaling to the enterprise presents challenges such as architecture constraints and increased monolithic application risks because of developer inexperience in application architecture patterns. Besides, most No-Code platforms require that a business deploy on their public cloud and don’t have deployment flexibility to a private cloud or on-premises infrastructure. From an extensibility perspective, because No-Code platforms lean towards operational efficiency use cases, they don’t possess the capability to focus on user experience and can’t connect to legacy systems.

An organization can gain application development efficiency, greater accuracy in delivered solutions, and time-to-value enhancing competitive advantage in the market. By adopting No-Code platforms and tools, a business organization can save time and resources because they don’t need to do custom software development.

Why No-Code App for Your Business Solutions

Suppose a business needs to build the best custom solution for business using No-Code. In that case, there is no need to compromise on the functionality because there will be no functionality gap with No-Code platforms. No need to worry about application updates and optimizations to run an application smoothly. 

No-code platforms decode business into more than 300 building blocks and enable users to build business management solutions. No-Code platform solutions can be used as the overall management layer in a company.

 Here are some of the solutions for small, medium, and large businesses:

 

Business Process Management Resolution with No-Code Platforms

Industry goals for business process management systems are dramatically changing. More and more businesses starting their digital transformation journey, and moving towards a customer-obsessed operating model digitizing all business functions is becoming parroted.

Sick of hiring Expensive Developers

If businesses are sick of hiring expensive developers to deploy processes, the number one reason organizations should choose No-Code platforms like FlowForma process automation is a fully No-code tool. Moreover, With FlowForma, a business does not need any IT skills to automate business processes. It empowers an organization to seamlessly deploy forms and workflows and manage business processes without any code.       

Traditional BPM vs FlowForma No-Code Tool

No-Code platforms empower the people who know this process is best to automate those processes with speed and flexibility; this is unheard of with traditional BPM tools and many other No-Code tools that require expensive coding specialists IT professional experience. No-Code, of course, leads to significant investment in time and money.

Speed and Deployment: It is also at least ten times faster in performance than other traditional BPM tools.

No hidden cost: There is no hidden cost when implementing the FlowForma process tool that needs an annual fixed license fee, which gives users unlimited document generation and endless process flow.        


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Amazon Lex – Features

Amazon Lex – Features

Amazon Lex’s latest core feature is sentiment analysis, which analyzes a text to understand that text’s emotion sends an appropriate response to the user. It is a machine learning-based feature that analyzes the user’s emotions like anger, happiness, and sadness. This feature is Amazon’s comprehension, a natural language processing service based on machine learning to understand the relationship between textual data and sentiment analysis. Amazon Lex Comprehend can also work for language detection, Topic modeling, and Key phrase extraction. 

Here are some points about more features of Amazon Lex to show its essentials for your business:

Text and speech-language understanding: Powered by the same technology as Alexa. Build once and integrate with multiple platforms

Designed for builders: efficient and intuitive tools to build conversation; scale automatically

Enterprise Ready: Scalable, Versioning, and alias support

Continuous Learning: Monitor and improve your bot.

Amazon Lex Customers

The chatbot feature is used by many organizations to uplift their business and boost their sales. It provides SDKs for Android, and IOS, which support speech and text input.

How Lex and Connect features are essential for your business?

If your chat can provide solutions for most of your customer’s requests, it will save your customers time, save time for waiting on hold while using a chatbot, and spend more time using your products. Moreover, you can integrate your Amazon Lex with your Facebook messenger, kick and Slack.

Integration of Amazon Connect and Lex

Amazon Connect is a cloud-based contact center that provides amazing customer service at a low cost. Besides, Lex provides a conversational interface using voice and text. With Amazon’s integration, Lex and Amazon Connect, you can uplift your business services by adding these advanced technologies of Lex’s automatic speech recognition (ASR) and natural language processing understanding (NLU). It will provide the best service experience for customers.

Amazon Connect

For further, Amazon Connect is a cloud-based contact center solution that scales to support a business of any size with tools that grow with your needs. It is easy to use and 100% cloud-based.

It has the following features:

Customer’s Challenges

Aging infrastructure:

This diagram shows the flow of new integration with Amazon Connect, Lex, and Lambda:

  1. At level 1, users call the customer service line to reschedule an appointment.
  2. At level 2, connect, call Lex service, and Amazon Lex will call lambda, then lambda call to the database to look up the customer information by phone number.
  3. At level 3 customer asks a question, which triggers lambda to summon the customer scheduling software to confirm the customer’s new appointment date per the customer’s request.
  4. Level 4 shows that Connect sends a confirmation message via SMS to the user using SMS software once an appointment date is confirmed.
  5. At level 5, the user receives appointment rescheduled details via text messages


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Lex Chatbot Ecosystem for Business

Due to the advent and rise of the Corona Virus disease of 2019, most countries have gone under lockdown, and all the corporate officers have adopted the work-from-home fashion. In such a scenario, cloud computing cut down costume servers and their maintenance cost and made it easier to scale the process. One of the market leaders for cloud computing is Amazon. Amazon’s one of the most powerful and extremely functional features that have come out of Amazon Web Services is a chatbot.  

What is a Chatbot?

A chatbot is an application that allows users to interact with an application as simple as an FAQ application. Where the user asks for a query, and the application comes back with the response. Using a chatbot, the user can have an interactive discussion instead of searching a website or application to get the information.

It’s no secret that many companies are trying to re-imagine how to improve their application user experience and build a conversational interface to interact with their application through voice or text commands.

AWS Lex Chatbot

AWS Lex Chatbot Ecosystem is a service for building conversational interfaces into any application using voice and text. Amazon Lex chatbot can build conversions using voice and text. AWS Lex uses advanced deep learning techniques like Natural Language Processing (NLP) to understand the meaning of the text and Automatic Speech Recognition (ASR). Moreover, users can convert speech to text. In simple terms, users have to create a chatbot according to their needs without any prior knowledge of complex technologies.

Organizations can also build powerful interfaces to use with mobile applications and build highly interactive and conversational user experiences for connective devices in the Internet of Things (IoT). It allows an organization to build enterprise chatbots to check sales data, marketing performance, and much more.

Benefits of AWS Lex Chatbot

How does Amazon Lex Chatbot Work?

When a Lex chatbot receives input, it either replies with a relevant message or completes the user’s desired task. This process triggers a lambda function that integrates with other services like dynamodb, S&S, poly, and many others services and performs the necessary action by providing the desired result.

The necessary steps to follow while working with Amazon Lex are as follows:

  1. Create a chatbot and configure it with intent, slots, and utterances.
  2. Test the bot on the text window slide provided by the Lex console.
  3. Publish a version and create an alias.
  4. Deploy the bot on suitable platforms.

Amazon bot: An artificial intelligence program that simulates an interactive conversation.

Intent: An intent represents an action that the user wants to perform; for example, if you’re going to order a service, then ordering the service is your intent and every intent has a descriptive name. It has utterances, which means how you want to convey your intent; for example, if you’re going to order a service, you can say, can I order service? Or I want to order a service.

Slots: Slots are pentameters that intent might require. For example, if a user wants to order or buy a service, they need to specify a service name, type, and other specifications that serve nothing but a slot.

Slot Type: Every slot has a slot type; you can create a built-in or custom slot type. For example, if you consider a service type, a business has development tools, databases, storage, networking, migration, etc.

Intent Fulfilment: It is nothing but how you want to fulfill the intent after the user provides the necessary information.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Connectivity and Security with IoT

The Internet of Things anticipates being ubiquitous in projected smart environments like homes, workplaces, and communities as they become more automated. IoT network effect and connectivity turn houses into intelligent, comfortable, and efficient living spaces as devices talk to other connected devices so that their functions work together efficiently. Without proper device configurations and security, the home network is open to compromise; here IoT Security provides the best solutions to prevent these risks.

Energy Management systems and households save energy and money by optimizing the use of electricity. However, these home devices can be abused by attackers to steal data and profile users. Routers, for instance, are the gateways to homes.

IoT Security

The Internet of Things and the exponential growth of associated technologies have connected devices. The range of IoT solutions and the number of diverse environments within which it helps conversely generates the potential for security breaches to establish across this heterogeneous web of connected things. We face a global scenario where security is no longer an option but still is a mandatory requirement.

There are three basic security premises to follow:

Prevention: Prevention deters attacks to avoid losses.

Detection: Detection identifies attacks to enable a rapid and thorough response.

Response: Respondents address and mitigate the incident as soon as possible in a structured way to minimize losses and allow a return to the regular operating business. There are three security levels of IoT security.

  1. Secure Network IoT Infrastructure:  This Security measure is used within communication such as virtual private networks dedicated to secure links.
  2. IoT Enablement Secure Layer: These include detecting replacement devices or a change in location for devices that should not move, such as a meter allowing to call only the authorized telephone numbers.
  3. Secure IoT Business IT and Devices: These extra security levels enable end-to-end security in our customers’ IT business and managed devices. It offers comprehensive and innovative solutions, including trusted public key infrastructure, and provides a unique digital identity.  


How IoT makes life easy for Consumers

Here is a massive risk of people’s data their privacy being invaded and maybe their data being locked up and encrypted; IoT makes life more convenient such as:

IoT Network with Connected Devices

The Internet of Things is a giant network with connected devices; these devices gather and share data about how they are used and the environment in which they operate.  It’s all done using sensors.

Sensors are used and embedded in every physical device such as mobile phones, electrical appliances, barcode sensors, traffic lights, and almost everything that comes across in day-to-day life. These sensors persistently discharge information about the working condition of the devices. IoT gives every device a distinct stage to dump their information and a specific language for all the devices to speak with one another. Information can be discharged from different sensors and shipped off IoT platform security. These platforms integrate the collected data from multiple sources, and other analytics can be performed on the data, and valuable information will extract as per requirement. Finally, the result is shared with other devices for better user experience, automation, and improving efficiencies.

IoT Operational Technology (OT)

Operation technology (OT) makes physical assets run throughout a variety of platforms. Without devoted security techniques, keen manufacturing plants can incline to business downtime.

For example, unstable mechanical switches can permit attackers to access machines like robots. They can tamper with commands and sabotage the production line. In large-scale IoT implementations like smart cities, environment frameworks like smart trash cans make waste management more efficient, smart kiosks give simple admittance to different public administrations. These Smart solutions are just a portion of the guaranteed benefits. IoT presents an abundance of possibilities to help ease day-to-day activities. Technologies are broadly controlled by sensors to assemble information and work seamlessly with others.

However, without prioritizing security, the IoT technology becomes open to abuse, and possible causes of users’ compromise the risk include monetary losses and legal concerns. A focus on security-by-design and the security development life cycle, and security technologies should protect users while still addressing convenience. Embracing IoT comes with risks, but they can be avoided.

IoT Cybersecurity should be integral in building smart communities because being secure is being smart.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Simple Notification Service Vs. Simple Queue Service

SNS stands for simple notification while SQS stands for simple queue service. SNS uses a publisher-subscriber system; for example, an owner owns a topic to publish, and subscribers get notified of events delivered to that topic. However, AWS Simple Queue Service (SQS) is a fully managed message queuing service. Here we are discussing AWS SNS Vs SQS,

AWS Simple Notification Service

SNS is a web service that coordinates handling the delivery or sending the messages to subscribing endpoints or clients. It is a fully managed and flexible service that eliminates a lot of overhead and complexity in managing mass public publication/subscriber distribution.

There are a couple of things that need to do to set up SNS are as follows:

Amazon SMS

In Amazon SMS, there are two types of clients, such as publishers, and subscribers also referred to as producers and consumers. The publishers communicate asynchronously with subscribers by producing and sending a message to a topic, which is a logical access point and communication channel. Subscribers like web servers share by using email addresses, Amazon SQS, and Amazon lambda functions or receive the message or notification over one of the supported protocols, including Amazon SQS, HTTP, or HTTPS, email, SMS, and lambda when they subscribed to the topic.

AWS Simple Notification Service (SNS)
Figure 1: AWS Simple Notification Service (SNS)

In this diagram, we can see in the center here is an SNS topic publisher pushes a message to the SMS topic, which is then publishing it to one or multiple subscribers, including lambda, SQS, HTTP, email, and SMS. When using SNS, you as, the owner, create a topic and control access to it by defining policies that determine which publishers and subscribers can communicate with a topic. A publisher sends messages to topics they have created, or they have permission to publish; instead of including a specific destination addressing each message, a publisher sends a message to the topic.

Amazon SNS matches the specific topic to a list of subscribers who have subscribed to that topic and delivers the message to each subscriber. Each topic has a unique name that identifies the Amazon SNS endpoints for the publisher to post messages and subscribers to register for notifications. Subscribers receive all messages published to the topics they subscribe to, and all subscribers to a topic receive the same messages.

AWS Simple Queue Service

It is a distributed system and serverless application. SQS makes it simple and cost-effective to decouple and coordinate the components of a cloud application. Using SQS, you can send, store, and receive messages between software components at any volume without losing messages and requiring other services to be always available.

Configuration of SQS with the following tasks:

Technical Comparison

AWS Simple Notification Service (SNS)

AWS Simple Queue Service (SQS)


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

OLTP vs. OLAP in Data Warehouse

What is OLAP?

OLAP stands for online analytical processing; it’s a category of software data for business decisions. OLAP allows users to analyze database information from multiple database systems online. A data warehouse is an example of an OLAP system.

The uses of   OLAP systems are as follows: 

A company might compare their sales in January with February then compare those results with another location stored in a separate database.

Amazon analyses purchases by its customers to produce a personalized homepage with products that are likely to interest their customers.

Advantages of OLAP System:

Disadvantages of OLAP System:

What is the OLTP System

OLTP or Online Transaction Processing, shortly known as OLTP, supports transaction-oriented applications in a 3-tier architecture. OLTP administers the day-to-day transaction of an organization. The primary objective of the OLTP system is data processing, not data analysis.

Example

An example of an OLTP system is an ATM center, which assumes that a couple has a joint account with a bank and one day both simultaneously reach different ATM centers at a precisely same time and want to withdrawal the total amount present in their account.  However, the individual that finishes the confirmation cycle first will have the option to get the money.

In this case, the OLTP system makes sure that the withdrawal amount will never be more than the amount in the bank. The key to note here is that OLTP frameworks are improved for conditional prevalence rather than data analysis. In addition, some other OLTP systems are online banking, online ticket booking, sending a text message, order entry, and many more. Online ticketing means that if two or more persons are booking a ticket in an airplane, the ticket will be reserved for the first in this process: first-come-first-serve the fastest fingers first.

Advantages of OLTP System

Here are some significant advantages of the OLTP System:

Disadvantages of OLTP System

The significant disadvantages of the OLTP system are as follows:

OLAP vs. OLTP

Figure 1: OLTP vs. OLAP

Furthermore, OLTP and OLAP systems are divided into different factors, which are business strategy master, data transactions, and analytics. OLAP is an information (Business data warehouse) based system, and OLTP is an operation (Business process) based system. 

We can differentiate OLTP and OLAP based on some parameters such as:

Parameters OLAP OLTP
Process Characterized by a large volume of data Characterized by a large number of short online transactions
Functionality Online database query management system Online databased modifying system
Method Uses data warehouse method Uses traditional RDBMS method
Query Mostly uses select operations Uses insert, update and delete information from a database
Style It can integrate different data sources for building a consolidated database It is designed to have a fast response time, low data redundancy, and normalized

Key Differences

OLAP OLTP
It is a category of software tools that analyze data stored in a database. It supports transections-oriented applications in a 3-tier architecture.
It creates a single platform for all business analysis needs, including planning, budgeting, forecasting, and analysis. It is useful for the day-to-day transactions of an organization.
A large volume of data characterized it. It is characterized by a large number of short online transactions.
OLAP’s data warehouse is created uniquely to integrate different data sources for building a consolidated database. OLTP uses a traditional database.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

 

Data Modeling and Data Warehousing

Data Warehouse Technique

The Data Warehouse technique is one of the hottest topics in business and data science. Data Warehouse is a source where an organization can structure its essential and best-quality data in one place. Companies store their valuable data assets, including customer data, sales data, and employee records data. Data Warehousing techniques generally utilize fundamental information detailing and investigation purposes.

There are a few characterizing highlights of data warehouse techniques, such as:

Subject Oriented: Subject Oriented means the information in the data warehouse revolves around some subjects. Accordingly, it does not contain all organizational data ever. A subject can be a specific business area in an organization, such as sales, marketing, and distribution. It helps to focus on modeling and analysis of data for decision-making.

Integration: Integration means each database, team, or even person has its preferences regarding naming conventions. That’s why common standards are designed to ensure that the data warehouse picks the best quality data from everywhere.

TimeVariant:  The Time Variants relate to the fact that a data warehouse contains historical data too. Hence, we mainly use a data warehouse for analysis and reporting, which implies we need to know what happened five or ten years back.

Non-Volatile: Typically, data in the Data Warehouse cannot be changed or deleted, but it can update through the update process is a little bit complicated. Previous data is not erased when new data is added to the data warehouse. Information is read-only and periodically refreshed.

Thus, Data warehouses are very well-structured and non-volatile single sources for companies’ data.

Data Modeling:

Why an organization needs data modeling? Here are some important points to show the importance of data modeling

The Warehousing technique allows the integration of data from multiple data sources such as web APIs, raw data, excel file data, cloud data, or data from a database. An organization collects data from various resources, integrated into a data warehouse in a single or consistent format.

Advantages of Data Warehouse

It is not an item that an organization can decide to acquire. Data Warehouse chooses and relies upon the company’s requirements.

Data Warehouse Technology and Business Intelligence

Business Intelligence is the demonstration of changing raw/operational data into useful information for business analysis.

How does it work

Business Intelligence on Data Warehouse technology extracts information from a company’s operational systems. The data is transformed (cleaned and integrated), and loaded into Data Warehouses, since this data is credible, and used for business insights.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Telnyx Call Control

What is Call Control:

Call Control is a programmable voice API. For instance, when a user contacts a call center, they encounter a recorded menu offering options such as pressing one for sales or support and two for agent support. Selecting agent support automatically transfers the user’s call to the appropriate department based on their selection. Telnyx enhances this experience with its advanced Call Control API.

Telnyx Call Control:

Many companies use embedded voice calling to make collaboration easy, but building and maintaining a voice application from scratch is complicated. It distracts developers from working on the core components for their solutions; that’s why developers turn to Telnyx rather than others. The Telnyx Call Control API allows users to incorporate voice workflows quickly within their applications. Their programmatic Call Control allows developers the building blocks to create customizable caller experiences.

A simple set of Call Control API commands controls the Telnyx communication engine, which returns webhook notifications to users’ systems. For example, you can build branching interactive voice response workflows from a single command.

When the user system sends a command, the Telnyx system begins monitoring for dial-tone responses. Each response will be forwarded to the user system as a webhook notification. The logic for administering the call is built into the application using Telnyx commands; they make the workflow responsive and sophisticated according to user requirements.

How Telnyx Call Control Different:

Telnyx Call Control provides its users total control over the call via API, whether you’re transferring, forking media for real-time audio processing, conferencing in several users, or enabling/disabling recording on the fly. Call Control allows you to embed customized communications workflows into your application.

Telnyx provides advanced features such as:

The above features show how data packets are transferred across the network and granular into the call flow. All the features mentioned above can be used by Telnyx out–off–the–box call control API.

API and Call Flow Control

Telnyx is one of the largest interconnected VoIP carriers companies in the country. Its call control system design makes it easier to embed voice capabilities and add advanced features like conferencing, call recording, and text-to-speech into the software application with a lot more to come.  Telnyx Call Control system brings advanced functionalities.

In addition, the advantage of leveraging the Call Control API is that application developers don’t need Telecom’s domain expertise to build great voice applications. It can be used to develop call center applications, call tracking apps, voice dialers, or even work for the staff.

API and Call Flow Control
Source: https://telnyx.com

Telnyx Programmatic Call Control Commands

Moreover, Call Control API offers the developer a handful of commands for some actions like answering an incoming call, dial a new call, or bridging an existing call; there is a webhook associated with each of these commands.

Telnyx Programmatic Call Control Commands

Building IVR with Call Control

Call center applications, call tracking apps, voice dialers, or workforce apps with embedded collaboration can leverage a Call Control API to build highly customizable caller experiences.

Indeed, a developer could build a widespread application called an interactive voice response system. Telnyx provides different Call Control solutions and commands. Moreover, Telnyx Telephony is a new infrastructure that defines network interface, network backbone, and phone numbers. 

Call Control: Carrier vs. Non-Carrier

Carrier Non-Carrier
Licensed as an interconnected VOIP carrier Application business that manages pass-through traffic
Manages a private network environment Reliant on the public internet for connectivity
Tier-1 privileges from PSTN operators and
owned phone number assets and routing control
Partners with telecom aggregators for communications functionality and numbers

Carrier: Carrier refers to a licensed company that operates on the same footing and with the same regulatory privileges as other traditional telephone companies. So, these companies typically manage their private network, and have direct interconnection and privileges with one PSTN operator. Therefore, these companies own their numbering assets and routing infrastructure.

Non-Carrier: However, Non-Carrier focuses on developing those APIs, better developer experience, call delivery, and providing numbers.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Telnyx Messaging and Numbers

Telnyx promises to provide the best technologies:

Telnyx claims that they are shaping the future of growth for all businesses with technology. It’s proved it by showing highly updated and delegated technologies that can fulfill all business needs. It is the first self-service, full-stack communications platform in the world.

SMS and MMS

We have gone about 25 years with texting, and from that time till now, texting expanded globally and advanced significantly. To make it more secure, Telnyx established an excellent communication channel for SMS and MMS. When a send message API request goes through by the user, SMS or MMS will be put into a queue by an API and forwarded to its destination. Telnyx’s latest feature, Message Detail Record (MDR), describes a specific message request, including active, pending, and completed message status.

Telnyx allows users to send and receive SMS and MMS programmatically. SMS provides a concise and engaging communication channel. MMS, on the other hand, used to send photos, videos, audios, or GIFs. It’s an excellent tool for creating a more engaging brand experience than static Taxes, and it’s also has a high engagement rate.

SMS Advantages

MMS Advantages

How Businesses Use Messaging

Businesses can effectively utilize messaging. Telnyx provides Messaging with a vast evolution beyond personal use and transformed into being used by companies. There are two types of customer messaging channels, P2P (person-to-person) and A2P (application-to-person), used for business communication.

Telynex brings three message-sending methods/types as long code, Toll-free, and short code:

Number Type Format Volume A2P vs. P2P Voice-Enabled Setup Time
Long Code 10-digit (U.S)
555-555-5555
one msg/sec P2P only Yes Instant
Toll-Free 10-digit (U.S)
800-555-5555
No limit P2P or a2P Yes Up to 48 hours
Short Code 5-or-6 digit 55555 No limit A2P only No Couple Weeks

Long Code: It’s a traditional ten digits number, and the most common type of number allows us to send one msg/second using a person-to-person use case.

Toll-Free: Toll-Free numbers are iconic numbers with 800 digits; every Toll-Free number usually will start with 8, such as 800, 833, 844. These types of numbers usually advertise for customer support. Toll-Free numbers are multifunctional. 

Short-code: These new types of five to six-digit codes are also perfect for utilizing marketing campaigns and promotions to boost sales by sending quick short messages to customers. Customers can also act on those messages.

Integrating SMS

Messaging Trends

Moreover, some trends provided by Telnyx like a real increased focus on regularity compliance there’s a real focus at the moment on protecting consumers from unsolicited marketing messages via SMS.

Telnyx Network Performance

Telnyx is an enterprise-grade carrier providing a platform and a private backbone supporting customers’ SMS and MMS with unique APIs. Moreover, customers can send their content more securely from point A to point B. In addition, for the secure transmission of content, Telnyx provided its own private and protected encrypted way to the end-users.  

Telnyx Python SDK

Telnyx also provides text messages with the Telnyx Python SDK. You can purchase a new number associated with a messaging profile that enables you to send messages with the Telnyx Python SDK. Telnyx Python SDK also provides national capabilities for SMS and MMS with A2P and international faculty with P2P.

In Summary, Telnyx’s simple solution for all contact centers is the number one choice for the SVCIT team compared to all other competitors out there with a lot more complex integration planning and staggering pricing model.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS-Container

What are AWS Containers?

AWS Containers are just isolation of processes and evolution of virtualization technology. Containers are virtual hardware and machines. With a virtual machine, an organization virtualizes operating systems, runs an application within the system, and the associated binaries and libraries required for the application within the environment.

AWS Docker Containers, on the other hand, is a little bit further up the stack; instead of virtualizing the operating system, they virtualized the application and the associated binaries and libraries for that application to run.

Benefits of AWS Containers:

Application Environment Components

The first thing we need for the application will be that runtime engine, whether that’s the java virtual machine, DotNET, or Node.js.

Different Environments

Therefore, for local development software or local development by using a personal laptop, there’s no guarantee that the libraries and the packages will be the same in the staging environment or the production environment or even on the On-Prem environment. They can be slightly or vastly different.

How do we sort this issue?

Docker to the rescue

AWS Docker Containers

What is Docker?

Moreover, Docker is a widespread platform for running containers. It’s known as a container platform and allows developers to run discrete units of code. So, the applications on a platform truly abstract the underlying operating system resources from the container itself.

Above all, you can run Docker on a physical operating system using physical machines. It also can be run on a virtual machine on the operating system. It is a client-server environment that consists of a Docker daemon that’s the Docker service, REST API, and command-line interface.  So that, it’s a relatively easy and reliable platform; its commands are straightforward to learn. Similarly, it offers many functionalities in terms of being able to run containers in different types of environments to network these containers together to keep them completely isolated.

AWS Docker Containers

Container and Docker Benefits

In SVCIT, we use AWS Docker containers to eliminate the verse on machine problems when working on code in joined environments with co-workers. Similarly, operators use AWS Docker containers to run and manage apps side-by-side in isolated containers to compute density better. An enterprise also uses it to build an agile software delivery pipeline to dispatch new highlights quicker, more safely, and with a high degree of operation certainty for both Linux and Windows.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS Elastic BeanstalkA developer wants to build a great website or any application. But, somehow always end up as a system administrator, you spend a lot of time managing and configuring servers’ databases, load balancer firewalls, and networks instead of coding. Whenever a developer has to scale servers to support more users, it becomes a headache because they have to spend extra time on the re-architecting whole infrastructure.

Challenges Faced by Developers

Challenges being faced by developers if they have to deploy their applications upon AWS platform:

With AWS Elastic Beanstalk, a developer just needs to focus on writing code and building a great product and hand over the provisioning and scaling of the infrastructure and the installation and management of the application stack to Aws Elastic Beanstalk.

Why AWS Elastic Beanstalk?

AWS Elastic Beanstalk is an easy-to-use service that deploys, manages, and scales web applications and services. Moreover, it’s instrumental in managing containers that support environments such as java.net, PHP, nodejs, Python, Ruby, and Docker on familiar servers such as Apache HTTP server, Apache tomcat nginx passenger, and IIS. Elastic Beanstalk supports familiar AWS services, for example, Amazon EC2, S3, simple alert service, Elastic load balancing, and auto-scaling. It’s easy to get started on the Elastic Beanstalk. Elastic Beanstalk is an example of a Platform as a Service (PaaS). With the AWS management console, the command-line interface and the API just need to choose a platform like nodejs or ruby, and Amazon EC2 instance type then selects additional resources to use, such as Amazon relational database service or Amazon virtual private cloud, and then just upload your code.

Elastic Beanstalk will handle the rest of the deployment details, such as provisioning load balancing auto-scaling and health monitoring applications. Elastic Beanstalk will automatically scale your application up and down based on easily adjustable auto-scaling settings. This service also allows us to retain full control over all the AWS resources powering an app. It also helps to take some or all of the resources of your infrastructure anytime. Elastic Beanstalk helps to focus on building great web or mobile apps without spending a lot of time managing and configuring infrastructure. It automatically load-balances and manages scale, helping to make sure an application is always available. It allows giving complete control under the hood by using any Elastic Beanstalk resource. There is no additional charge for Elastic Beanstalk; just need to pay only for the AWS resources required to store and run applications.

Elastic Beanstalk Basic Components

Application

A Logical collection of Elastic Beanstalk components, including:

Application Version

Environment  

How does it Work?

Elastic Beanstalk

Beanstalk Deployment Options for Update

All-at-once (deploy all in one go): Fastest, but occurrences aren’t accessible to serve traffic for a bit (downtime) 

Rolling: Update a couple of instances at a time (batch), and afterward move onto the next batch once the first batch is healthy

Rolling with additional batches: Like rolling, however, spins up new instances to move the batch (so that the old application is still available)

Immutable: Spins up new instances in a new ASG, deploys version to these instances, and then swaps all the instances when everything is healthy

Traffic split: The underlying level of approaching customer traffic that Elastic Beanstalk movements to condition occurrences running the new application variant you’re sending.

Traffic parting assessment time: The timeframe, in minutes, that Elastic Beanstalk waits after an underlying healthy deployment before proceeding to move all approaching customer traffic to the new application form that you’re deploying.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

AWS CloudFormation for business

Without AWS CloudFormation, an organization may manage its infrastructure using run books and scripts to create and manage everything. In this way, version controlling and keeping track of changes can be challenging. Things get even more problematic when they need to replicate the entire production stack multiple times for development and testing purposes or every other purpose. But, provisioning an infrastructure stack directly from a collection of the script isn’t simple.

Wouldn’t it be great if your cloud creates and manage your infrastructure and application stack in a controlled and predictable way with exact consistency?

What is CloudFormation?

AWS CloudFormation is assistance that encourages you to model and set up your Amazon web administration assets. Thus, they need to invest less energy in dealing with those assets and possess more power for zeroing in on your applications that run in AWS.

Introducing AWS Cloud Formation

CloudFormation provisions and manages stacks of AWS resources based on templates. An organization can create and model infrastructure architecture using AWS CloudFormation. This architecture allows you to handle anything from a single Amazon ec2 instance to a complex multi-tier, multi-region application. CloudFormation can be used to define simple things like an Amazon VPC subnet as well as provisioning services such as AWS opsworks or AWS Elastic Beanstalk. It’s easy to get started with cloud formation.

Simply, a JSON file will be a blueprint to define the configuration of all the AWS resources that make up the business infrastructure. You can select a template that CloudFormation provides for commonly used architectures such as a lamp stack running on Amazon ec2 and Amazon RDS. Then just need to upload your template. CloudFormation will provision and configure your AWS resources stack. It allows updating the stack at any time by uploading a modified template through the AWS management console, command line, or SDK. CloudFormation also allows keeping track of all changes made by infrastructure and application stack. Moreover, the CloudFormation version of the control infrastructure architecture is the same way as working with software code.

Template:

 A template is an AWS cloud arrangement designed in JSON or YAML language that narrates your AWS infrastructure. These templates comprise nine principal objects:

1.  Format Version
2.  Description
3.  Metadata
4.  Parameters
5.  Mappings
6.  Conditions
7.  Transform
8.  Resources
9.  Outputs

Provisioning Infrastructure is as simple as creating and uploading a template to CoudFormation this makes replicating a business infrastructure very simple.  A business with AWS can easily and quickly spin up a replica of a production stack for development and testing with a few clicks in the AWS management console. An organization can tear down and rebuild the replicas stacks whenever requested.

Replicating a production stack cloud has been time-consuming and error-prone if you did it manually. However, with cloud development, you can make and oversee AWS asset stacks rapidly and dependably, and there is no extra charge for cloud formation. So, an organization just needs to pay only for AWS resources that cloud formation creates and your application uses.  

Stack:

Window Stack

CloudFormation access control

IAM Users Access

With IAM, Cloud Formation can have access control for users and ensure that only IAM users can create, update, and delete stacks. 


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.
 

Silicon Valley Cloud IT company provides the  Enterprise software development and Application Integration to introduces new business model for organizations.

 

Importance of Enterprise Software Development Solutions

Enterprise software development solutions help organizations to face gradual changes in the latest business trends. Because it affects the organization’s growth directly. Now organizations need to adopt new technologies. Here, the Enterprise Software Development solutions help organizations to grow. Because of the ESD solutions for new business models, even small businesses can grow easily.

Why need to adopt Enterprise Architecture (EA) solutions to navigate all complexity?

Here, we discuss why organizations need enterprise architecture.

So, a business has many attributes like process, products, data, people, technologies, etc. These attributes provide benefits to the business. Especially for startups.

How these business parts fit together?

So, this is the purpose of enterprise architecture. As an enterprise, architecture is a conceptual framework that describes how the business is constructed. ESD Solution’s principles can be applied to any business entity. Similarly, business as a government, a non-profit business, or even a loose enterprise that’s interested in say “solving a problem like a world hunger”. Various associations need to get that, how their business can grow in this condition where they have to achieve complex activities. Moreover, medium and small-scale organizations can adopt these architectures.

The reason behind why enterprise solutions became more popular: Because the enterprise provides solutions to replace the customizable programs and the complexity of tools replace specialty with common business applications and development tools. Our aim is to improve enterprise productivity and efficiency by providing business logic and support functionality, by implementing enterprise solutions in different steps.

Characteristics of Enterprise Application Integration are as follows:

Building the bridge between business and technology:

In addition, ESDS can promote business through innovation and fundamental approaches. It helps a business to gain unique customer insights and preparing an organization for the future.

Challenges in Implementations

Without considering the right decision for a business an organization may have to face some difficulties. That can increase the failure possibility of the business. So that without a good enterprise solution business has to confront the accompanying difficulties:

  1. Excessive customization
  2. The dilemma of Internal Integration
  3. Poor understanding of business implications and requirements
  4. Lack of challenges management
  5. Poor data quality
  6. Misalignments of IT with business
  7. Hidden costs
  8. Limited training
  9. Lack of top management support 


Enterprise solutions will unpack not only the requirements in the business process flow but also going to get acceptance and understanding. So, by adopting the real convenient way for the development process to raise an organization. Moreover by using enterprise solutions businesses can reduce the cost of hiring a team to manage security, database, storage space, data management, and development for scratch.

Therefore term Enterprise Software Development Solutions is not just for the implementation of a system. It is a go-to framework. Similarly, the enterprise solutions solve the conflict of different operation systems, like database handling, and legacy systems.

Major advantages of Enterprise application Integration

Hence, EAI solutions are more suitable for an enterprise having heterogeneous systems. It works as middleware.

Typical Domains of Enterprise Architecture:

How one delivery channel is working with .net, java, or some other technology?

For the Integration of enterprise software, so there is no need to care about the technology. Therefore, that’s the powerful advantage of EAI solutions it can be integrated with all types of technologies. Specifically, some major examples of enterprise software development tools are Trello, Workflow, and Slack tool.

Where Enterprise Application Integration solutions fit?

In the same way, companies like Insurance companies, health care networks, and companies with multiple heterogeneous systems should consider EAI solutions. So, here it makes sense for them to implement. Hence, by implementing EAI solutions companies can reduce their cost and save their time by avoiding try and testing errors.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Hiring Process Protects SVCIT

 

Hiring Process of SVCIT

The future of any business is determined by its hiring process. As a business, the ability to adopt the right hiring process will help in positioning your business for success. This is due to the ability of your business to attract talents that perform at their best toward business growth and profitability. Our good Hiring Process Protects SVCIT culture.

Being a custom software development enterprise company, the hiring process protects SVCIT as it has a direct impact on not only our business but our clients. Of course, just like every other business, your hiring process must be divisible into these four stages;

Definition of Role To Be Filled for the hiring process

As an enterprise software development firm, we only hire talents who have at least 5 to 7 years of experience in the field. We look out for engineers with experience, expertise, and skills that are needed to perform at the high-level other engineers perform at SVCIT.

That’s why we are able to bring them on board as fast as possible. We immerse our engineers into the system by not treating them as newbies. They are assigned the complex jobs available at that point immediately since they have passed very tough interviews already.

Sourcing, Marketing, and Promoting Open Positions

The hiring process of SVCIT is the access we have to over 1200 engineers in a sister company. Outside of this, our HR team has access to professional recruiters in the field and well-known resume banks. From there, we can reach out to suitable and qualified engineers for an interview.

The hiring process at SVCIT is often around two weeks, and another two weeks of onboarding in order to get the engineer to settle in SVCIT culture and modus operandi.

Select and Evaluate Engineers For The Position

The limited number of engineers have made it to this stage. We are able to avoid the problems other firms encounter during the hiring process since we know why we are hiring and who fits into the picture.

Our access to pre-screened resources also gives us an edge and speeds up the hiring process and meets the qualifications we are looking for.

Verification of Skills

We assigned the toughest tasks to the new engineers available at that point. However, regardless of how they perform, we avoid making the new hire feel less of themselves. The SVCIT culture assumes that we’ve not done enough during the onboarding process if an engineer fails.

What’s Unique About the SVCIT Hiring Process?

Quite a lot stands us out from the bandwagon approach to hiring talents. Firstly, we have in place a human resource team that is able to identify talents from the first interaction. This is due to their experience in the field.

Another important aspect of our hiring process that is a result of our culture is how we promote teamwork. The new engineer is the responsibility of every member of the team. It’s solely their fault if the new member is unable to get on board and up to speed in a given time.

Despite assigning complex tasks, the expectations are often at their lowest. We believe we have a system in place to bring the new engineers up to speed. Therefore, we often do not blame the engineer for their failure. Rather, the fault is on the management who has failed in hiring the right asset, or we assume we are unable to provide the engineer with the resources to succeed.

Final Words

At SVCIT, we abide by the use of checklists. Therefore, it will be a near impossibility not to be able to hire the right talent or fail in providing them with the right resources to succeed. Our approach to hiring is to help engineers succeed from the onset and benefit our clients in the long term.


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Nonvalue-added Activities in Software Development

 

Clients approach us with so many different requests with expectations for pure profitability and sometimes magic. In most cases, we accept medium to large-size refactoring software projects with the planning of complete redesign and development.

However, the critical success of refactoring software is to consider eliminating nonvalue-added activities that are increasing the corporation’s cost. Here is a direct relation between Non-value-added Activities in Software Development cost and corporation cost. So, it’s more important to eliminate Non-value-added Activities in Software Development

In cost accounting, managers use financial details to make a valued decision. These decisions will help to reduce unnecessary costs in activities. Every activity in an organization has a cost, and this is a fact. There is no free activity, even though it is internal. It can even be an internal process of software that is using CPU time or other resources. In small cases, this might not be a considerable amount. But in large-scale software, the processing time can be significant due to overhead creation. For example, resources used to maintain and monitor marginal CPU performance as well as IT experts added to the overhead. As a result, to reduce a cost, the activity associated with that cost must be clear.

What are the nonvalue-added activities in Software Development?

Innovative and evolving organizations realize that after a few years, many of the software in use must go through the refactoring process to stay competitive. However, most managers consider refactoring for the performance upgrade or adding new functionalities. While missing the point of eliminating nonvalue-added parts that have no benefit to the overall company activities but cause an unwanted cost.

For example, in one of the recent SVCIT refactoring project, our team were able to recognize merging multiple reporting systems into a new licensed integrated system. The refactoring outcome reduced two IT resources, over five x-large AWS instances, saved 70% in processing time, and a couple of licenses in use. Which decreased $1.5M annually in total cost to the company.

Over 40% of the generated reporting system belonged to another profit center in the organization that was shut down over two years ago. Still, no one attempted to stop the activity to eliminate unnecessary high costs.

The Cost is not an Expense

The organization might incur costs even though there are no expenses recorded in the accounting system. For example, if the user gives up a purchase and does not continue with the transaction due to a lack of performance of the system or an inconvenient process in the user interface, then that examines cost due to lost sales.

Another example of opportunity cost is the wasted time of engineers and other resources that maintain the non-active part of the software or resources employed by the non-active component. Note, this cost will never be measured or accounted for in the accounting systems. You can also consider this as opportunity costs vs. outlay costs.

However, this is one of the most critical losses of revenue that will never account for in a software problem. 

Loss of Resources Due to Depreciated Software

When managers allow the depreciated component of a software system to continue working as mentioned above. It is a nonvalue-added activity that incurs a cost, according to the accounting system, the definition of cost means the company will take away opportunities, and resources available to be able to add more value to other parts of the organization.

it is a Nonvalue-added Activity in Software Development that incurs cost. according to the accounting system, the definition of cost means the company will take away opportunities, and resources available to be able to add more value to other parts of the organization. That means if these non-value-added activities do not get recognized by experts, they hold back companies eventually from adding value-added activities and allow competitors with such expertise to advance into the market. Therefore, removing costs is not the solution to your growth. Eliminating undesirable parts in your product items and repeating them now. And again maybe the way you may be searching for to upgrade, develop, and make the item proficient.


Author: Cyrus Akbarpour
Copyright Silicon Valley Cloud IT, LLC.

Coding Standards Scroll

Just like any other industry, the Covid-19 outbreak is sure to change things in the software industry. Right before the pandemic began, the software industry experienced undeniable advancement with the release of new and better technologies by the hour. Now that the pandemic struck, can the industry still testify to this? Will custom development dwindles or will it be enhanced? What will be the new custom software Development Trends?

Well even in times of crisis like this, the software industry still proves strong. Digital classrooms and online groceries simply became more popular. Video conferencing became the savior of many top companies as they had to close offices. Now, whether you are a business owner or a software developer. You should start looking forward to what software would define a Post-COVID world. Here we discuss 5 custom software development trends post covid-19.

1. Artificial Intelligence

Right before the start of COVID-19, artificial intelligence posed a promising future. With many industries firing their workers during the pandemic, it may mean that the future is now. Due to the life lived during the pandemic, many businesses will no longer see the need to take their workers back.

Many would continue operations just as they did during the pandemic, and many would opt for artificial intelligence in handling various tasks. Robots and chatbots would become the new normal with many industries adopting this over human labor. With this, AI/ML development would soon become a trend even though it had been during the Pre-COVID era.

2. Virtual Reality and Augmented Reality

Now, it shouldn’t be a surprise that VR development would be among the trend. It has been one thing that has helped most people cope during the pandemic. Since the world is locked down and people have to stay indoors, most people have secretly done many things and visited many places with the help of their VR glasses.

 In a Post-COVID world, there would be an increase in the demand for virtual reality. It would become a trend in first-world countries with everyone having a desire to virtualize reality.

3. Cloud Computing

In today’s digital world, we mostly live in the cloud. In a Post-COVID world, cloud computing would be encouraged because it makes files, servers, and databases available to any authorized user through any device, at any place, and at any time. During the pandemic, industries that already use cloud computing were able to continue operations smoothly from the cloud while being at home.

The ability to control infrastructural and operational costs is another good reason why industries would consider cloud computing. Flexibility also stands as one of the numerous benefits allowing businesses to upgrade or downgrade according to what your business needs at the moment.

4. IoT

Who doesn’t love smart devices? We all do, and we are going to get lots of smart devices in the Post-COVID era. With the 5G technology, you might one day be able to start your car by touching the fingerprint sensor on your mobile phone while sitting on the 50th floor of your office building.

Smart refrigerators could begin ordering groceries from online retailers with no effort from you at all. In a post-COVID world, there would be massive advancement in Internet of Things solutions.

5. Open Source Software

In times when even top brands are struggling to keep profits up, open-source software has gained popularity. Why? You may ask. Well, these industries are doing whatever it takes to reduce their expenses while maximizing profit. 

A simple way of doing this is to utilize the power of open-source software. It saves them the cost of buying licensed software, and it produces the same or even better results at some point in time.

As we always say in SVCIT, Stay Innovative, and Stay Tuned…


Author: SVCIT Editorial
Copyright Silicon Valley Cloud IT, LLC.

Svcit Silicon Valley Cloud IT LLC. + 1 (855)-MYSVCIT Customers@SiliconValleyCloudIT.com