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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

Baby elephant.JPGThe past year has been dominated by Big Data.  What it might mean and the way you might look at it.  The stories have often revolved around Hadoop and his herd of oddly-named chums.  Vendors and analysts alike have run away and joined this ever-growing and rapidly moving circus.  And yet, as we saw in our own EMA and 9sight Big Data Survey, businesses are on a somewhat different tour.  Of course, they are walking with the elephants, but many so-called Big Data projects have more to do with more traditional data types, i.e. relationally structured, but bigger or requiring faster access.  And in these instances, the need is for Big Analytics, rather than Big Data.  The value comes from what you do with it, not how big it happens to be.

Which brings us to Big Blue.  I've been reading IBM's PureSystems announcement today.  The press release headline trumpets Big Data (as well as Cloud), but the focus from a data aspect is on the deep analysis of highly structured, relational information with a substantial upgrade of the PureData for Analytics System, based on Netezza technology, first announced less than four months ago.  The emphasis on analytics, relational data and the evolving technology is worth exploring.

Back in September 2010, when IBM announced the acquisition of Netezza, there was much speculation about how the Netezza products would be positioned within IBM's data management and data warehousing portfolios that included DB2 (in a number of varieties), TM1 and Informix.  Would the Netezza technology be merged into DB2?  Would it continue as an independent product?  Would it, perhaps, die?  I opined that Netezza, with its hardware-based acceleration, was a good match for IBM who understood the benefits of microcode and dedicated hardware components for specific tasks, such as the field programmable gate array (FPGA), used to minimize the bottleneck between disk and memory.  It seems I was right in that; not only has Netezza survived as an independent platform, as the basis for the PureData System for Analytics, but also being integrated behind DB2 for z/OS in the IBM DB2 Analytics Accelerator.

Today's announcement of the PureData System for Analytics N2001 is, at heart, a performance and efficiency upgrade to the original N1001 product, offering a 3x performance improvement and 50% greater capacity for the same power consumption.  The improvements come from a move to smaller, higher capacity and faster disk drives and faster FPGAs.  With a fully loaded system capable of handling a petabyte or more of user data (depending on compression ratio achieved), we are clearly talking big data.  The technology is purely relational.  And a customer example from the State University of New York, Buffalo quotes a reduction in run time for complex analytics on medical records from 27 hours to 12 minutes (the prior platform is not named).  So, this system, like competing Analytic Appliances from other vendors, is fast.  Perhaps we should be using images of cheetahs?

[The photo is from my visit to Addo Game Reserve in South Africa last week.  For concerned animal lovers, she did eventually manage to climb out...]

Posted February 5, 2013 10:30 AM
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