As part of an effort to make it easier for organizations that have invested in DB2 to work collaboratively with Big Data platforms such as Hadoop, IBM today announced that it is integrating versions 10 of DB2 and its InfoSphere data warehouse with Hadoop. In addition, IBM is also announcing that version 10 of these offerings is now available on Windows, UNIX and Linux systems, including versions of Linux on IBM mainframes. Previously, version 10 of DB2 was only available on the z/OS for mainframe systems.
What's important about this is that structured and unstructured data management are getting more federated as organizations look to correlate and analyze information stored in multiple formats. In addition to managing application workloads that span multiple platforms, Bernie Spang, director of strategy and marketing for IBM Software, adds that the people inside IT are increasingly being asked to manage both SQL and NoSQL environments such as Hadoop.
Beyond the performance capabilities of DB2 10, Spang says what will distinguish IBM most going forward are the management tools it provides to simplify the management of data. In addition to requiring a smaller infrastructure footprint to achieve higher levels of performance than rival database offerings that also require more software licenses, Spang says that DB2 10 and other IBM data management technologies cost a lot less to own and manage.
In fact, IBM claims that data warehouse queries run up to 10 times faster than rival offerings, and that the company's compression technology frees up storage space by up to 90 percent to dramatically reduce storage sprawl. To prove that, IBM is inviting customers to get acquainted with a free Express edition of DB2, adds Spang.
Clearly, we're living in a time of great change as it applies to data management, which includes everything from the advent of Hadoop to the rise of in-memory computing models. The challenge for IT organizations is going to be figuring not only how to apply those technologies to solve some of their thornier data management issues, but to also how do it in a way that ultimately reduces the real costs of processing all that data.