UPDATED 05:42 EDT / NOVEMBER 06 2012

Don’t Replace SQL: Big Data Tech is Supplemental, Says IBM Executive

Bernie Sprang, the executive in charge of database software for IBM’s software group, hopped into theCube at IOD 2012 to talk about the technological side of the big data PureData, his company’s response to this trend. He shared his insights with theCube hosts, SiliconAngle founder John Furrier and Wikibon’s Dave Vellante.

Sprang says that databases have come a long way in the past few years. Moore’s law, together with mobile and a great deal of innovation on the software side of analytics, have created a big opportunity to simplify IT infrastructure, and specialized solutions are the way to go.

The executive says that NoSQL and Hadoop are much better equipped to handle unstructured data than relational database. But he stresses that SQL is still the best choice for a broad range of traditional workloads, which is why these new technologies should be considered a supplement rather than an alternative.  This approach of products tailored to specific workload works, and IBM is doing it too.

Sprang goes on to talk about PureData, the latest addition to IBM’s PureSystem appliance line-up. PureData comes in three configurations: one optimized for transactional workloads, one for analytics and a third configuration for operational analytics, also known as real-time.  The first and latter systems are both based on the company’s D2 database technology.

Sprang believes that this is the main thing that sets IBM apart from its competitor. Instead of offering a general purpose solution that makes many compromises to accommodate a wide variety of workloads, IBM sells different tools for specific use cases and adds plenty of integration on top.

The executive says that the idea behind PureSystems is to save companies the time and money while reducing the chance for human error that is associated with buying components and putting them together in-house. That way, he says, organizations can spend less on operations and  funnel more resources into their big data efforts.


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