Data source identification
Identifying the relevant data sources from which to extract data can be challenging, especially when dealing with large and complex datasets. Data engineers need to determine which sources are required for the analysis or processing and which specific data fields are needed.
Data source compatibility
Different data sources may have different formats, data types, and data structures, which can make it difficult to extract data in a uniform way. Ensuring compatibility between the data sources and the ETL system requires significant planning and development.
Data quality issues
Data quality issues such as missing, inconsistent, or inaccurate data can arise during the extraction process. These issues can have significant downstream impacts on the accuracy and reliability of the data.
Extracting large amounts of data from multiple sources can put a strain on the ETL system and cause performance issues such as slow data extraction, network congestion, and resource contention.
Extracting data from different sources can pose data security risks if the data is not properly protected.
Data governance and access
Determining ownership and access rights to the data sources can be complex, especially in large organizations with multiple stakeholders.
Inaccurate data mapping
One of the most common mistakes in ETL design is inaccurate data mapping.
ETL processes must be thoroughly tested before deployment to ensure that they are working correctly. Inadequate testing can result in data errors, processing delays, and other issues that can impact the accuracy and reliability of the data.
Poor data quality
ETL processes can be impacted by poor data quality, including incomplete data, inconsistent data, and inaccurate data. This can result in incorrect data being loaded into the target system, and can impact downstream analysis and decision-making.
Lack of scalability
ETL processes must be able to handle large volumes of data, and must be scalable to accommodate future growth.
Range of engineering and domain expert constant requirement. For data management process an organization will require to have the given below resource at minimum.
The SVCIT Automatic Data Integration & Management Service is a scalable solution that eliminates the need for traditional ELT solutions, enabling businesses to handle increasing data sources, volumes, and complexity with ease. Our service can help you complete your digital modernization or transformation in weeks instead of months.
AXYS PLATFORM IS A GAME-CHANGING SOLUTION FOR ORGANIZATIONS STRUGGLING WITH DATA SILOS AND EXPENSIVE DATA WAREHOUSES.
By leveraging the Axys Data Ops & Data Fabric, SVCIT simplifies the complexities of managing and accessing data from various sources. With virtualization technology, Axys creates a single, unified view of all data across the organization, making it easier to access, analyze, and visualize data more efficiently and effectively or to be used for any solutions.
WITH SVCIT & AXYS CENTRALIZE DATA PIPELINING AND CUSTOM DATA SOLUTIONS, UNLOCK VALUABLE INSIGHTS INSTANTLY.
The Axys platform seamlessly connects to multiple data sources, including databases, data lakes, and cloud platforms, and integrates data from various applications and systems. It also offers robust data access control and governance features to maintain data quality, security, and compliance. Moreover, the platform provides advanced analytics capabilities such as predictive modeling and machine learning (ML), empowering businesses to gain insights and make data-driven decisions. Additionally, data visualization tools facilitate the creation and sharing of dashboards and reports.