Contrasting data virtualization and data movement

While data virtualization is a great solution for several scenarios, there are some cases where a data movement pipeline is preferred. Data virtualization interrogates the data source at query time, so you see the latest, freshest state of the data. However, your queries are limited to data available at query time and you are dependent upon the source system for row versioning. What should you do when you need to perform historic analysis over time? When a data source doesn't support historic states of the data, you need to curate this data using a data movement approach.

Even when the data is available, data virtualization provides a more limited set of data transformation capabilities compared to a data movement strategy. While you can implement some rudimentary data quality rules in your query, if the data itself requires significant cleansing or transformation, then a data movement approach offers ultimate flexibility for...