A business view of distributed data that uses query federation to join data from heterogeneous systems in real time. Data virtualization shields business users from the complexity of backend systems, eliminates the need to move or copy data, and gives data administrators the ability to change those systems without impacting downstream reports and applications.
The value of data virtualization technology is summarized in five broad use cases: Light-speed business, Bridging relational data islands, Creating deep context, Agile activities, and Historical support.
Data preparation technology has, as a result, expanded to include virtualization (aka federation) techniques, where data is accessed in situ when required and cleansed en route to the requesting application or user.
The main idea of data virtualization is to provide a unified, abstracted, and encapsulated view of data coming from a single or heterogeneous set of data stores [1, 2]. For this, it facilitates the concept of virtualization, which is very common in the IT industry where you find all kinds of virtualized resources, e.g. multiple virtual machines that share one physical infrastructure.