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Data Virtualization Adoption Propelled by Significant Business Benefits

Faster, cheaper, better...data virtualization middleware platforms provide critical data integration capabilities

Enterprise adoption of data virtualization accelerated in 2011 propelled by organizations growing need for greater business agility, lower costs and better performance.

These benefits were fully described in an earlier series of articles:




The success of data virtualization can now be observed across hundreds of organizations and is clearly evident in the ten case studies described in the recently published Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility.

In this article, I will describe data virtualization at a high level and explain how data virtualization technology works.

What is Data Virtualization

Data virtualization is a data integration approach and technology used by innovative organizations to achieve greater business agility and reduce costs.

Data virtualization technology is a form of middleware that leverages high-performance software and an advanced computing architecture to integrate and deliver to both internal and external consumers data from multiple, disparate sources in a loosely coupled, logically-federated manner.

By implementing a virtual data integration layer between data consumers and existing data sources, the organization avoids the need for physical data consolidation and replicated data storage. Thus, data virtualization enables the organization to accelerate delivery of new and revised business solutions while also reducing both initial and ongoing solution costs.

Most front-end business applications, including BI, analytics and transaction systems, can access data through the data virtualization layer. Consumption is on demand from the original data sources, including transaction systems, operational data stores, data warehouses and marts, big data, external data sources and more.

High performance query algorithms and other optimization techniques ensure timely, up-to-the-minute data delivery.

Logical data models, in the form of tabular or hierarchical schemas, ensure data quality and completeness.

Standard APIs and an open architecture simplify the consumer-to-middleware-to-data source connections.

Data virtualization middleware platforms provide the functionality described above within integrated offerings that support the full software development life cycle, high-performance run-time execution and reliable, 24x7x365 operation.

How Data Virtualization Technology Works

The primary objects created and used in data virtualization are views and data services.

These objects encapsulate the logic necessary to access, federate, transform and abstract source data and deliver the data to consumers.

These objects can vary in scope and function depending on the business need, canonical information standards and other usage objectives. Individual objects can call other objects in order to perform additional functions. This is often done using a layered, or hierarchical, approach where objects that perform application delivery functions call objects that perform transformation and conformance functions which, in turn, call objects that perform source data access and validation functions.

The ability to reuse common objects in this way provides flexibility, accelerates new development and reduces costs.

The grouping of objects related to a single domain or subject area, such as trades in financial services or projects in research and development, can be used to create the data virtualization equivalent of a subject-oriented data mart. Multiple domains can then be combined to create the virtual equivalent of a data warehouse.

As a result, data virtualization can be adopted in a phased manner, starting with a narrow set of application use cases and expanding over time to a wider, enterprise-scale adoption.

A data virtualization platform consists of three primary middleware components that perform a full range of development, run time and management functions. These include:

  • Integrated Development Environment
  • Data Virtualization Server Environment
  • Management Environment

Integrated Development Environment

Data virtualization technology includes an integrated development environment (IDE) that can be used by a range of people, from business analysts to application developers, to define and implement the appropriate view and data service objects.

The foundation of these views and services is an underlying logical data model that is, in turn, based on either a tabular or hierarchical schema. Data quality requirements, such as standards conformance, enrichment, augmentation, validation and masking; and security controls (e.g., authentication and authorization) can also be also implemented within these object definitions.

The IDE includes profiling-like introspection and relationship discovery capabilities designed to simplify each developer's understanding of existing data sources and jump-start the modeling process.

To limit the coding required and save development time, drag and- drop modeling techniques and a rich set of pre-built, any-to-any transformations automatically generate view or data service objects. Multiple languages (SQL, XQuery, Java, etc.) can extend these capabilities to address more advanced data virtualization needs.

Standard source and consumer APIs, based on ODBC, JDBC, SOAP, REST, etc., simplify source data access and consumer delivery development activities.

Integrated data governance, including lineage and where used, metadata asset management and versioning provide needed controls.

Data Virtualization Server Environment

In data virtualization, run-time activities are typically triggered by queries, or requests for data, from a consuming application. The data virtualization server is the component that executes these queries.

The query engine within the server, which is specifically designed to process federated queries across multiple sources in a wide-area network, optimizes and executes queries across one or more data sources as defined by the view or data service.

Cost- and rule-based optimizers automatically calculate the best query plan for each individual query from a wide variety of supported join techniques. Parallel processing, predicate push-down, scan multiplexing and constraint propagation techniques optimize database and network resources.

The data virtualization server also does the following:

  • Transforms query results sets to ensure that the data is complete, high quality and consumable by the user.
  • Executes authentication and authorization security functions to protect data from improper use.
  • Caches appropriate data sets to enhance both performance and availability.

To complete the query, the server delivers the results directly to the consuming application and logs all activities.

Management Environment

Data virtualization servers are configured for development, testing, staging, production, back-up and failover operations.

To manage this topology, meet service-level agreements (SLAs) and ensure reliable 24x7x365 operations, the data virtualization platform also includes a complete set of integrated management tools.

These integrated tools support all the activities required to set up the data virtualization middleware and users, including provisioning the software, granting access to sources, integrating with LDAP and other security tools, etc.

System management tools manage server sessions and resources.

Monitoring tools log activities, monitor memory and CPU usage, as well as display key health indicators in dashboards.

Optional clustering tools improve workload sharing and synchronization across servers.

Data Virtualization Platform Examples

A number of enterprise software vendors provide data virtualization technology.

Several of these solutions are delivered as extensions to other technology platforms, such as BI, ETL or an enterprise service bus (ESB).

Others, such as the Composite Data Virtualization Platform from Composite Software, are complete, standalone data virtualization platforms.

Conclusion

With increasing pressure to move faster, save money and perform better, organizations have adopted data virtualization technology with successful results.

Data virtualization middleware platforms provide critical data integration capabilities that support the full software development life cycle, high-performance run-time execution and reliable, 24x7x365 operation.

When evaluating data virtualization offerings, different vendors have taken different approaches.  The best selection will require you consider not only functional capabilities, but also domain expertise and complementary services that each vendor can provide.

And finally, check references.  Real users doing real work is the best test.

Editor's Note: Robert Eve is the co-author, along with Judith R. Davis, of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book published on the topic of data virtualization.  This article includes excerpts from the book.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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