Data virtualization – accessing data anywhere
Historically, enterprises have consolidated data from multiple sources into central data stores, such as data marts, data warehouses, and data lakes, for analysis. While this is still very relevant for certain use cases, the time, money, and resources required make it prohibitive to scale every time a business user or data scientist needs new data. Extracting, transforming, and consolidating data is resource-intensive, expensive, and time-consuming and can be avoided through data virtualization.
Data virtualization enables users to tap into data at the source, removing complexity and the manual processes of data governance and security, as well as incremental storage requirements. This also helps simplify application development and infuses agility. Extract, Transform, and Load (ETL), on the other hand, is helpful for complex transformational processes and nicely complements data virtualization, which allows users to bypass many...
