Data Virtualization and ETL (Extract Transform Load) are often pitted as enemies, with ETL users and vendors often opposing adoption of Data Virtualization. The truth is this – almost 100% of Data Virtualization adopters also have ETL and continue to use both effectively. Those who have not adopted Data Virtualization are losing out. Extract Transform Load is needed for large bulk movement of data. Data Virtualization delivers information access faster. Together they support the broader goals of business insight, analytics and reporting:
There are several more scenarios for Extract Transform Load and Data Virtualization to collaborate. Architecturally speaking, when Data Virtualization is below ETL, the virtual data views are source for Extract Transform Load. When Data Virtualization is above, it uses the secondary or derived data sources populated by ETL to create virtual data services. And when used side-by-side they create hybrid integration patterns.
Learn more about Data Virtualization and ETL (Extract Transform Load) with this whitepaper.