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: Subject Oriented means the information in the data warehouse revolves around some subjects. Accordingly, it does not contain all organization data ever. A subject can be a specific business area in an organization, such as sales, marketing, and distributions. It helps to focus on modeling and analysis of data for decision-making.
Integration: Integration means each database, team, or even person has their preferences regarding naming conventions. That’s why common standards design to ensure that the data warehouse picks the best quality data from everywhere.
Time–Variant: The Time Variants relates 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 change or delete, 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 source for companies’ data.
Why an organization needs data modeling? Here are some important points to show the importance of data modeling:
- A model is an abstraction or representation of a real-world object. The data model acts as a blueprint for the developers in building the warehouse.
- The tables that make up the warehouse are; the relationship between the tables, the primary keys, and the foreign keys are list in the model.
- The data warehouse system is long term system. Developers and molders change, but the company will be using it for a long time.
- Proper documentation is one of the significant problems in the IT industry.
- Assume that there is no data model, and the developers start building the warehouse. There is a big chance that some entities may be left, and then the developers have to start from scratch.
- Imagine this happening in a big company. There would be huge losses.
- Thus a model should be made and discussed with all investors, so everybody is in the same spot, and everyone agrees to the design.
The Warehousing technique allows integrating data from multiple data sources such as the web APIs, the 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
- Fundamental inquiries can be answered by studying trends
- Data Warehousing technique is quicker and more precise
- Store a large amount of data, including real-time and historical data
- Provides links, tables, and the relation between various tables, which make data access easy
It is not an item that an organization can decide to acquire. Data Warehouse chooses and relies upon according to 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 transformed (cleaned and integrated), and loaded into Data Warehouses, since this data is credible, used for business insights.
Author: SVCIT Editorial
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