What is Data virtualization

Transformation in technologies creating lots of changes in every related area. Artificial Intelligence, Machine Learning and Data-Science created a new kind of concept  in data platform, that is "Data Virtualization" .  By name it look like it might be related to server or network, but it is concept and logical method of data management.

Data virtualization is the method of manipulating data from various sources to develop a common, logical and virtual view of information so that it can be accessed by front-end solutions such as applications, dashboards and portals without having to know the data's exact storage location.

What are the benefits of Data virtualization?

·         Data virtualization increases revenues.

·         Data virtualization lowers costs.

·         Data virtualization reduces risks

·         It is much faster way to manage data.

·         It complements traditional data-warehouse

·         It maximize performance of overall information system environment.

·         Data virtualization automatically enables self-service BI.

·         It goes far beyond data federation.

·         Data virtualization is more agile than traditional method.

·         Data virtualization is the right context for Big Data and Data Lake.

The process of data virtualization involves

·         Abstracting

·         Transforming

·         Federating

·         Delivering of final data.

Data virtualization software is often used in tasks such as:

·         Data integration

·         Business integration

·         Service-oriented architecture data services

·         Enterprise search

Data virtualization allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located. To resolve differences in source and consumer formats and semantics, various abstraction and transformation techniques are used. This concept and software is a subset of data integration and is commonly used within business intelligence, service-oriented architecture data services, cloud computing, enterprise search, and master data management.

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