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Revolution R Enterprise Update Boosts Big Data Analytics Capabilities

Revolution R Enterprise 6.0 features improve performance and scalability, add new data access options.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Revolution Analytics, a commercial provider of software, services, and support for the open source R project, today announced the general availability of Revolution R Enterprise 6.0, its commercial-grade analytics software built upon the open source R statistics language for R-based enterprise-class data analytics.

The new iteration of Revolution R Enterprise delivers improved performance, productivity, and enterprise readiness for statistical analysis of very large data sets. R developers are now equipped with the robust tools required to meet the growing demands and requirements of modern data-driven businesses.

Using the built-in RevoScaleR package in Revolution R Enterprise, R users can process, visualize, and model terabyte-class data sets in a fraction of the time of legacy products without requiring expensive or specialized hardware.

Key highlights of Revolution R Enterprise 6.0 include:

  • Platform LSF cluster support: Now supports distributed computing on multi-node Platform LSF grids. Support on Windows-based grids provided via Microsoft HPC Server

  • Cloud-based analytics with Azure Burst: Switch computations from a local Microsoft Windows HPC Server cluster to the Azure Cloud with a single command

  • Big-Data Generalized Linear Models: Supports big-data predictive models used in insurance, finance, and biotech industries; use a multi-node server or distributed grid for fast analytics on big data

  • Direct analysis of SAS, SPSS, ASCII, and ODBC data: Analyze proprietary data formats without the need for SAS/SPSS licenses

  • Updated R 2.14.2 engine: Improves performance and parallel programming capabilities; Revolution Analytics’ open-source RHadoop project (for Hadoop integration) is updated to work with this new engine

More information is available at

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