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Data Virtualization in the Enterprise

Data Virtualization has several capabilities that allow it to perform different roles in delivering information agility:

  • Rapid access to new data
  • Data abstraction (of original or derived sources or other middleware)
  • Data federation  / Real-time integration
  • Data services provisioning
  • Unified virtual source of reference for data discovery, unified governance, etc.

Therefore it is often not possible to fix its place in the enterprise architecture. Rather it varies depending on a broad spectrum of use case patterns it supports (see patterns and architecture for Analytical, Operational, Web/Cloud, Data Management use cases).

However the "hour glass" architecture below provides a broad generalization across use cases. Data Virtualization plays a role in the bottom, middle and top of the enterprise information architecture and it can do all three simultaneously:

  • Data source virtualization - At the bottom of the architecture (not shown) it is used to connect and abstract any data source - primary or secondary, internal or external, structured or unstructured, operational or analytical. So it sits above the sources to provide virtual source views and is accessed by other middleware including ETL, ESB, applications, portals, etc.
  • Data integration - At this level data virtualization / data federation provides the choice of virtual real-time data integration, versus physical replication (ETL, data replication) or physical data propagation (ESB, Services Integration (SOA)).  Projects and processes would choose one or more integration styles depending on needs.  While the other styles also require a physical end point (data store or application) to put the integrated data, data virtualization is self-contained and provides integrated canonical business views without need for a physical data store.
  • Data services or application format mappings - This allows all data to be exposed through a data services layer following the service-oriented principles of reuse and flexibility. Data services can take many forms: SQL-based services for BI/Analytic queries;  SOAP-based data services work together with application and business process services and a services security and management layer to create a complete SOA architecture. Of growing importance is REST architectures commonly used for mobile and cloud applications for which data virtualization enables fully REST-ful Linked Data Services. And other formats such as B2B services, semantic data services, portlets, widgets, etc.

Vision for agile info architecture - many to many

Image based on Hourglass architecture in the Forrester´s  "Data Virtualization Reaches Critical Mass" report


Data Virtualization and Other Data Management Tools

Data virtualization is essential for business agility. But it does not replace the need to persist historical data, connect applications, improve source data quality, and so on. It has to work with other data management and middleware tools to bring out the most cost-effective and agile solutions to problems. Here are some ways it does that: