Data virtualization versus ETL – when to use what?
Historically, Data warehouses and data lakes are built by moving data in bulk using ETL. One of the leading ETL products in the market happens to be from IBM and is called IBM DataStage. So, it begs the question as to when someone should use data virtualization versus an ETL offering. The answer depends on the use case. If the intent is to explore and analyze small sets of data in real time and where data can change every few minutes or hours, data virtualization is recommended. Please note that the reference to small sets of data alludes to the actual data that's transferred, not the dataset that a query is performed on. On the flip side, if the use case requires processing huge datasets across multiple sources and where data is more or less static over time (historical datasets), an ETL-based solution is highly recommended.
