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Revolution Analytics Unlocks SAS(r) Data for R

Revolution R Enterprise 4.2 Now Supports SAS Data Files, Opens up Migration Path to R

Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language, today released Revolution R Enterprise 4.2, which, for the first time, adds the ability to natively import data from SAS files into the R environment without data size limitations. Now, companies looking to apply the advanced statistical capabilities of R to data locked in the SAS environment can easily import the data into Revolution R Enterprise.

With this landmark capability in version 4.2, Revolution Analytics also launched a SAS to R Challenge for SAS customers in the United States. Revolution Analytics will convert SAS code to R free of charge to qualified participants, who can then run statistical analyses of big data sets on Revolution R Enterprise.

"Through conversations with CIOs over the past year, it's become clear to us that there's increasing frustration and dissatisfaction with legacy data analytics vendors for their inflexibility, high licensing costs and pricey hardware requirements," said Norman Nie, Revolution Analytics CEO. "Hundreds of companies have lost control of their data to SAS, and now, with Revolution R Enterprise, they finally have a means to unlock this data. We invite SAS customers to take us up on our challenge and experience just how fast and affordable Revolution R Enterprise can be, even for big data sets."

The ability to read and migrate SAS files enables companies to transition from legacy systems to the R environment. With Revolution R Enterprise, users will get the same speed, if not better, when performing analyses of large data sets and at largely reduced licensing costs. Furthermore, analysis with Revolution R Enterprise can be run on commodity servers, saving users the expense of purchasing specialized servers.

Revolution R Enterprise builds on open source R with innovations in big data analysis, integration and user experience to meet the demands of business environments:

  • Fast, scalable and extensible big data analysis for the R language with the RevoScaleR packages, including out-of-memory parallelized statistical algorithms for linear regression, logistic regression and multidimensional cross-tabulations (with more to come in future releases);
  • An enterprise-ready platform for server-based R deployment, with the RevoDeployR Web Services architecture; and
  • A full-featured integrated development environment with visual debugging capabilities for R programmers on the Windows platform.

Revolution R Enterprise remains on an accelerated development roadmap to bring further performance, productivity and enterprise-readiness to open source R. Today's update includes the following new features:

  • Direct import of SAS data sets into the native, efficient XDF file format
  • Direct import of fixed-format text data files into XDF file format
  • New commands to read subsets of rows and variables from XDF files in memory;
  • Many enhancements to the R Productivity Environment (RPE) for Windows
  • Expanded and updated user documentation
  • Added support on Linux for the big-data statistics package RevoScaleR
  • Added support on Windows for Web Services integration of predictive analytics with RevoDeployR.

Revolution R Enterprise 4.2 is available immediately for 64-bit Red Hat Enterprise Linux systems and both 32-bit and 64-bit Windows systems. Pricing starts at $1,000 per single-user workstation. Server pricing is available on request.

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About Revolution Analytics

Revolution Analytics ( delivers advanced analytics software at half the cost of existing solutions. By building on open source R--the world's most powerful statistics software--with innovations in big data analysis, integration and user experience, Revolution Analytics meets the demands and requirements of modern data-driven businesses. Headquartered in Palo Alto, Calif., Revolution Analytics is backed by North Bridge Venture Partners and Intel Capital.

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Jasmine Teer
Page One PR for Revolution Analytics
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