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As a proven data integration technique, data virtualization is being increasingly adopted by enterprises and government agencies seeking to complement their earlier data consolidation and data synchronization investments. Moreover, data virtualization usage has recently accelerated as a way to gain tangible revenue, productivity, compliance and cost benefits.



A number of data virtualization usage patterns have emerged as its adoption moves beyond individual projects to enterprise-wide deployments. Common to all is the need to rapidly overcome data complexity, location, structure, completeness and latency challenges. This article identifies eight popular patterns along with typical use-case scenarios that address day-to-day business problems.

Data virtualization at a glance

Data virtualization middleware integrates data from multiple, disparate sources -- anywhere across the extended enterprise -- in a unified, logically virtualized manner for consumption by nearly any front-end business solution, including reports, portals, mashups, applications, search and more. By accessing the data from both original and already consolidated data warehouse sources, data virtualization avoids the need for additional physical consolidation and replicated storage of source data, making it faster to build and lowering the cost to operate when compared to data-warehouse-only integration approaches.

Developers can create semantic abstractions in the form of relational views or data services. At run time, user-level applications, reports or mash-ups call upon these on-demand data services to provide the requested data. A high-performance data virtualization server optimizes access to the data, as well as queries, federates, abstracts and delivers this data to the consuming applications.

Operational transparency is a key feature, enabling the data virtualization middleware to fit easily into any existing IT environment. Rapid time to solution, lower total cost of ownership, and effective leveraging of existing staff and technology resources are the primary benefits that data virtualization delivers.

Eight popular data virtualization patterns

Since its introduction, data virtualization use has evolved across several dimensions. Scale of deployment is one dimension, evolving from initial projects to wider multi-project deployments. Support for new technology initiatives is another, evolving to support large-scale virtualization, service-oriented architecture (SOA) and cloud computing initiatives.

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