Data virtualization

Data virtualization is any approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted or where it is physically located.[1]

Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data, thus reducing the risk of data errors and reducing the workload of moving data around that may never be used.

Unlike Data Federation it does not attempt to impose a single data model on the data (heterogeneous data). The technology also supports the writing of transaction data updates back to the source systems.[2]

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.

Examples

Functionality

Data Virtualization software is an enabling technology which provides some or all of the following capabilities:

Data virtualization software may include functions for development, operation, and/or management.

Benefits

Drawbacks

Data Virtualization Platforms

Available data virtualization platforms include:

Reference Books

History

Enterprise Information Integration (EII), first coined by Metamatrix, now known as Red Hat JBoss Data Virtualization, and data federation have been used by some vendors to describe a core element of data virtualization: the capability to create relational JOINs in a federated VIEW.

See also

References

  1. "What is Data Virtualization?", Margaret Rouse, TechTarget.com, retrieved 19 August 2013
  2. 1 2 3 "Data virtualisation on rise as ETL alternative for data integration" Gareth Morgan, Computer Weekly, retrieved 19 August 2013
  3. "Rapid Access to Disparate Data Across Projects Without Rework" Informatica, retrieved 19 August 2013
  4. Data virtualization: 6 best practices to help the business 'get it' Joe McKendrick, ZDNet, 27 October 2011
  5. pros reveal benefits, drawbacks of data virtualization software" Mark Brunelli, SearchDataManagement, 11 October 2012
  6. 1 2 3 "The Pros and Cons of Data Virtualization" Loraine Lawson, BusinessEdge, 7 October 2011
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