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IBM Exec Offers Deeper Look at DB2 10

IBM Exec Offers Deeper Look at DB2 10
By Jennifer LeClaire

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IBM DB2 10 integration with Hadoop-based systems is a different approach for analyzing structured and non-structured information. Over the last few years, we have entered into a new era of data management where the answer isn't automatically "get me a relational data system," said IBM's Bernie Spang.
 



IBM earlier this month unveiled its first update of DB2 software in four years. With the new database software, IBM is on a mission to tame the data deluge.

We caught up with Bernie Spang, director of strategy, marketing and database software and systems to discuss the latest iteration of DB2, how it's attracting customers from Oracle and some practical examples of DB2 10 in action.

Q: How much success are you having getting database clients to migrate away from Oracle?

A: We're seeing success continue to accelerate quarter after quarter. Clients are looking for alternatives. They have been, in fact, for some time but many have not seriously considered moving because of the perception that it's complicated, risky, costly, etc.

With the compatibility for Oracle database applications that we introduced in the previous version of DB2, we really changed that equation. With DB2 9 we also upped the game for optimization for SAP application workloads. We're starting to see that snowball effect really taking hold.

Q: With DB2 10, you stressed a few specific benefits. One of them was accessing Big Data for deeper insights. How deep are we going with this? What's different or new in DB2 10?

A: Hadoop-based systems use a different approach, a file-based distributive approach for analyzing structured and non-structured information. And that's important. In the last few years we have entered into a new era of data management where the answer isn't automatically "get me a relational data system." It's "get me the right system that best handles and can analyze this particular type of data."

In this Big Data era where we are dealing with the high-volume, velocity, and variety of data we don't want to create silos of analyses and offer different results in different pieces back to the business user to put together. We don't want to recreate the problem that was caused in the early days of business computing where each different application has its own database, a known data structure, and that has to be integrated. That becomes a big project.

In the era of Big Data analytics, we want to be smarter than that. We want to think ahead. We want to model all the insights from these different systems to be able to easily flow together. By combining them you get even greater insights, new insights. New enhanced integration enables you to come from within the warehouse to access the insights that you're generating from Hadoop-based systems, bring those to the warehouse and add them to the mix of your analyses of the structured data. Then you can feed back insights to the business user, a more insightful answer. (continued...)

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