Predictive Analysis on the Mainframe

Real time, high volume predictive analysis has become a hot topic with the growing interest in Big Data. The consulting firm McKinsey addresses the growth of Big Data here. McKinsey’s gurus note that advancing technologies and their swift adoption are upending traditional business models. BottomlineIT also took up Big Data in October 2011.

With that in mind, IBM has been positioning the zEnterprise, its latest mainframe, for a key role in data analysis.  To that end, it acquired SPSS and Cognos and made sure they ran on the mainframe. The growing interest in Big Data and real-time data analytics fueled by reports like that above from McKinsey only affirmed IBM’s belief that as far as data analytics goes the zEnterprise is poised to take the spotlight. This is not completely new; BottomlineIT’s sister blog, DancingDinosaur, addressed it back in October 2009.

Over the last several decades people would laugh if a CIO suggested a mainframe for data analysis beyond the standard canned system reporting.  For ad-hoc querying, multi-dimensional analysis, and data visualization you needed distributed systems running a variety of specialized GUI tools. Still, the resulting queries could take days to run.

In a recent analyst briefing, Alan Meyer, IBM’s senior manager for Data Warehousing on the System z, built the case for a different style of data analysis on the zEnterprise. He drew a picture of companies needing to make better informed decisions at the point of engagement while applications and business users increasingly are demanding the latest data faster than ever. At the same time there is no letup in pressure to lower cost, reduce complexity, and improve efficiency.

So what’s stopping companies from doing near real-time analytics and the big data thing? The culprits, according to Meyer, are duplicate data infrastructures, the complexity of integrating multiple IT environments, inconsistent security, and insufficient processing power, especially when having to handle large volumes of data fast. The old approach clearly is too slow and costly.

The zEnterprise, it turns out, is an ideal vehicle for today’s demanding analytics.  It is architected for on-demand processing through pre-installed capacity paid for only when activated and allowing the addition of processors, disk, and memory without taking the system offline.  Virtualized top to bottom, zEnterprise delivers the desired isolation while prioritization controls let you identify the most critical queries and workloads. Its industry-leading processors ensure that the most complex queries run fast, and low latency enables near real-time analysis. Finally, multiple deployment options means you can start with a low-end z114 and grow through a fully configured z196 combined with a zBX loaded with blades.

Last October the company unveiled the IBM DB2 Analytics Accelerator (IDAA), a revamped version on the Smart Analytics Optimizer available only for the zEnterprise, along with a host of other analytics tools under the smarter computing banner. But the IDAA is IBM’s analytics crown jewel. The IDAA incorporates Netezza, an analytics engine that speeds complex analytics through in-memory processing combined with a highly intelligent query optimizer. When run in conjunction with DB2 also residing on the zEnterprise, the results can be astonishing, with queries that normally require a few hours completed in just a few seconds, 1000 times faster according to some early users.

Netezza, when deployed as an appliance, streamlines database performance through hardware acceleration and optimization for deep analytics, multifaceted reporting, and complex queries. When embedded in the zEnterprise, it delivers the same kind of performance for mixed workloads—operational transaction systems, data warehouse, operational data stores, and consolidated data marts—but with the z’s extremely high availability, security, and recoverability. As a natural extension of the zEnterprise, where the data already resides in DB2 and the OLTP systems, IDAA is able to deliver pervasive analytics across the organization while further speeding performance and ease of deployment and administration.

Of course, IT has other options: EMC now offers its Greenplum data analytics appliance  and Oracle just released its Big Data Appliance. Neither requires a mainframe. But when the price for the latest general purpose mainframe starts at $75,000 and can do much more than any appliance, maybe it’s time to consider one. The value of the predictive, near real-time business analytics alone could justify it.

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