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Data Virtualization: The Next Wave in the Virtualization Revolution
By Robert Eve
Virtualization is changing computing as we know it. A primary factor in this change stems from virtualization’s key strength of hiding the physical characteristics of computing resources from the way in which other systems, applications and end-users interact with those resources.
Server virtualization was the first step, delivering greater computing power along with greater adaptability. Storage virtualization followed, enabling data centers to keep pace with ever-increasing storage and service-level demands. Bringing virtualization out of the data center, application and desktop virtualization simplify desktop configuration, thereby cutting management costs. The common thread across these disparate applications is virtualization’s ability to overcome hardware and software complexity and deliver improved agility and significant cost savings.
Data Virtualization: The Next Wave
Recently, virtualization efforts have focused on data itself. Alternatively referred to as a virtualized data layer, an information grid or information fabric, data virtualization brings together data from multiple, disparate sources - anywhere across the extended enterpris - into a unified, logical, virtualized data layer for consumption by nearly any front-end business solution including portals, reports, applications and more.
Figure 1. Data Virtualization at a Glance (Courtesy of Composite Software)
The Revolution Drivers
The same forces that drove storage, application and desktop virtualization are behind the data virtualization wave as well. These include:
- Overcoming complexity: This is especially important as data silos continue to proliferate, and data structures and formats diversify.
- Accelerating time to solution: With business pressures increasing, IT is under the gun to deliver new capabilities more quickly than ever. Data is often the biggest bottleneck in new application development.
- Responding to cost pressures: Squeezing more return from existing assets while reducing future spends applies to both hardware and software, as well as to the data itself. The ability to leverage data assets cost effectively is critical.
- Recognizing need for new approaches: Traditional data integration approaches such as file transfers, database replication and ETL attempt to overcome complexity through replication rather than virtualization. Unable to meet time-to-solution and cost-savings goals, they often exacerbate complexity problems by creating even more silos.
How Data Virtualization Works
Data virtualization is enabled by middleware known variously as distributed query, virtual data federation or enterprise information integration (EII). Today’s data virtualization technology provides three key capabilities:
- Data virtualization serves up data as if it is available from one virtual source, regardless of how it is physically distributed across data silos. Query optimization and caching enable the high performance required to meet latency objectives without physical replication.
- Data abstraction simplifies complex data by transforming it from its native structure and syntax into reusable views and Web services that are easy for applications developers to understand and the applications themselves to consume. Common higher-level abstractions might include customers, invoices, shipments, payments and more.
- Data federation securely accesses diverse operational and historical data, combining it into more complete and meaningful information for a range of application uses.
At build time, data virtualization middleware provides an easy-to-use data modeler and code generator that leverage enterprise metadata to create abstracted relational views or Web data services of source data.
At run time, the consuming applications call the data virtualization middleware that executes high-performance queries to securely access, federate, transform and deliver the required data in real-time.
By avoiding physical data replication with its associated storage and support requirements, data virtualization reduces development time and ongoing costs. Further, as requirements change or expand, modifying the models and regenerating the virtual data can be completed in minutes, without requiring IT resources to physically rebuild extracts or marts. Both the business and IT share in these benefits.
Data Virtualization in Action
Enterprises apply data virtualization to a wide range of business requirements in a range of industries. One recent example occurred in the financial services industry where investment managers responsible for large-equity portfolios used data virtualization to help improve their investment decision making. At this firm, managers team with financial analysts to build portfolio analysis models that leverage a wide range of equity financial data from a number of financial research databases. To hide the complexity of the source data and make it easier and faster for the analysts to access the right data, this investment firm created a data virtualization layer that abstracts the data into a set of high-performance views shared by a number of analysts and models. This enabled analysts to spend more time on analysis and less on access, thereby improving portfolio returns. Further, virtualization enabled loose coupling of the sources and consumers, thereby improving IT flexibility when several source systems were retired or replaced by outside financial information services.
Getting started
Nearly every project in the typical IT project portfolio requires some form of data integration and can therefore serve as the starting point for testing the benefits of data virtualization. Best candidates are projects with high source data complexity that require rapid time to solution, near-real-time access and reasonable transformation. As time and cost savings are realized from the initial project, the next step is typically to expand across an entire division or enterprise.
Join the Revolution
Just as server, storage, applications and desktop virtualization have exploded on the IT scene and delivered significant value in a few short years, the time is right for data virtualization. Data virtualization is proving a superior data integration method because it handles today’s increased complexity, and addresses the needs for agility and cost efficiency. Join the revolution!
About the Author
Robert “Bob” Eve, vice president of marketing of Composite Software, has held executive-level marketing and business development roles at several leading enterprise software companies and is a frequent contributor to industry publications.For information about Composite, go to www.compositesw.com.
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