Big data, cheap storage bring in-memory analytics into spotlight

By Allen Bernard, CIO |  Big Data, Analytics, in-memory database

If you're paying attention to big data, lately you've probably heard terms such as in-memory analytics or in-memory technologies. Like many tech trends that appear new only because their histories are obscured by newer and sexier tech, or because time has yet to catch up with them&mdashserver virtualization and the cloud are just reinventions from the mainframe days, after all-in-memory is a term being resurrected by two trends today: big data and cheap, fast commodity storage, particularly DRAM.

"In-memory has been around a long, long time," says Dave Smith vice president of marketing for Revolution Analytics, a commercial provider of software, services and support for R, the open source programming language underpinning much of the predictive analytics landscape. "Now that we have big data, it's only the availability of terabyte (TB) systems and massive parallel processing [that makes] in-memory more interesting."

If you haven't already, you'll start to see offerings, including SAP HANA and Oracle Exalytics, which aim to bring big data and analytics together on the same box. Or you can also get HANA as a platform supported in the cloud by Amazon Web Services or SAP's NetWeaver platform, which includes Java and some middleware.

News: SAP Puts HANA In-Memory Database on Amazon Web Services

Meanwhile, analytics providers from SAS, Cognos, Pentaho, Tableau and Jaspersoft have all rolled out offerings to take advantage of the in-memory buzz, even if some of these offerings are mere bolt-ons to their existing product suite, says Gary Nakamura, general manager of in-memory database player Terracotta, a SoftwareAG company.


Originally published on CIO |  Click here to read the original story.
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