Revolution Analytics and IBM Netezza Partner to Offer Enterprise Open Source Statistics Language

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Revolution Analytics, a commercial provider of software and services based on the open source R project for statistical computing, and IBM Netezza announced they are teaming up to integrate Revolution R Enterprise and the IBM Netezza TwinFin Data Warehouse Appliance. According to the vendors, this will enable customers to directly leverage the capabilities of the open source R statistics language as they run high-performance predictive analytics from within data warehouse platforms.

The commercial, enterprise-ready version of R will be ported over and optimized to run advanced analytics in-database in the Netezza data warehouse appliance, Matthew Rollender, director of technology and Strategic Alliances of IBM Netezza, tells DBTA. "Just by having that analytics and that advanced processing in the database, you are removing a lot of data movement and latency and it also gives you the ability to better manage and provision data with respect to the advanced analytics." The mechanism on the Netezza side that enables this is the i-class technology, which is similar to an SDK that enables the movement of advanced analytics and computational functions down into the database in a way that is transparent to the user, explains Rollender.

With Revolution R Enterprise for IBM Netezza, advanced R computations are available for rapid analysis of hundreds of terabyte-class data volumes - and can deliver 10-100x performance improvements at a fraction of the cost of traditional analytics solutions, the vendors claim. IBM and Revolution Analytics say R is used by more than two million analysts in academia and at companies such as Google, Bank of America and Acxiom.

"We are entering the age of analytics, where companies have been collecting all of this data, and it is growing exponentially," says Jeff Erhardt, COO of Revolution Analytics. At the same time, companies are realizing that if they are not able to make business-driven decisions from this data, they are not competitive, he adds. In addition, there are smaller companies, or large companies' departments that have been priced out of the market because of legacy software costs they were not able to afford. The current combination of the massive amounts of data that companies need to analyze; students that were trained on R in school; and corporations demanding something more cost-effective, is creating "a perfect storm" leading to R, asserts Erhardt.

More information is available from IBM Netezza and Revolution Analytics.