In today´s world of rapid decision making the concept of data extraction and loading is not always the most efficient way to get the most current data. Data virtualization is the rapid access to live data via solutions that provide a single data management model (Metadata Model ) that allows an application to retrieve and manipulate data from one or more data sources without requiring technical details about the data, such as how it is formatted or where it is physically located.

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.

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.

An example use cases could be a pharmaceutical company implemented a data virtualization tool from to enable its researchers to quickly combine data from both internal and external sources into a searchable virtual data store.

CONNX Data Virtualization uses the CONNX DB Adaptor to create a single view of a single or multiple databases and allow real-time access to consume the data as it is being updated in real-time.

The concept of Data Virtualization is the ability of an enabling technology to provide, Virtualized Data Access — Connect to one or more (similar or different) data sources and allow the user to consume the data from a single data access point. Use cases of Data Virtualization software may include functions for development, operation, and/or management.

Benefits

• Fewer data errors
• Less stress on source systems workload
• Reduced time for development/support
• Less strain on data storage
• Sped up data access with real-time