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Data Virtualization: How It Works

Data Virtualization delivers abstracted and integrated information in real-time from disparate sources to multiple applications and users. But it is also easy to build, easy to consume, and much less effort to maintain.

Build Virtual Data Services

In order to build Virtual Data Services, the user follows three simple steps:

  1. Connect & Virtualize Any Source. Quickly access disparate structured and unstructured data sources using included connectors. Introspect their metadata and expose as normalized source views in the data virtualization layer
  2. Combine & Integrate into Business Data Views. Combine, integrate, transform, cleanse source views into canonical model-driven business views of data -  do it entirely in a GUI or through documented scripting.
  3. Publish & Secure Data Services. Any of the virtual data views can be secured and published as SQL views or dozen other data services formats.

Discover and Consume Integrated Information

  • Global Metadata / Data discovery. Global information search capability allows any user or application to discover, search, browse and eventually query both metadata and data through virtual data services to retrieve information.
  • Hybrid Query Optimization. The best DV platforms utilize a combination of real-time query optimization and rewriting, intelligent caching, and selective data movement to achieve superior response and performance against both on-demand pull and scheduled batch push data requests.
  • Integrated Business Information. Data virtualization delivers integrated information while hiding complexities of accessing disparate data. Users and applications get what they want, in the format they want, with real-time high performance.

Perform Data Governance and  DV Management

  • Data Governance. DV layer serves as a flexible and unified layer to expose business metadata to users. At the same time it helps to understand the underlying data layers through data profiling, data lineage, change impact analysis and other tools and expose needs for data normalization / quality in underlying sources. Thus DV can be the  "single point of reference" to govern information.
  • Security and Service Level Policy. All data views from source level to canonical business views to data services can be secured and authenticated to users, groups and roles at highly granular view-row-column level. Further custom security and access policies can throttle or manage service levels to protect source systems from overuse.
  • Monitoring and Management. Leading DV platforms will include several monitors, dashboards, audit logs, and management consoles to ensure smooth operation of the DV solution. It also provides tools for managing clusters, high availability, users/roles and to migrate virtual data between development, test and production.

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