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Data Virtualization

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.

Added Perspectives
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.
- Barry Devlin in Data Virtualization’s Extending Reach September 29, 2015
(Blog)
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.
- Barry Devlin in Automating Data Preparation in a Digitalized World, Part 4 July 17, 2016
(Blog)
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.
- Julian Ereth in Data Virtualization in Business Intelligence and Analytics November 7, 2016
(Blog)
Other Relevant Content
Podcast
Andrew Sohn: The Promise of Data Virtualization
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