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Revolution R Enterprise DistributedR

Portable Power: Big Data Analytics For the Entire IT Infrastructure

DistributedR Enables Revolution R Enterprise To Run On Diverse Architectures from Windows Laptops and Compute Clusters to EDWs and Hadoop.

Revolution R Enterprise DistributedR is a parallel computing framework responsible for managing compute resources used by Revolution R Enterprise.

By providing consistent management of memory, cores, processors, threads and servers, DistributedR enables R scripts to take advantage of multiple architectures.

DistributedR Provides A Consistent, Portable Platform for R Analytics

In order to support systems as divergent as Teradata Database, various Hadoop distributions and Microsoft HPC, Revolution R Enterprise DistributedR provides consistent task execution and data communications.  With Revolution R Enterprise DistributedR, new platforms do not require new versions of Revolution R Enterprise ScaleR, assuring:

  • Completeness:  Virtually all of Revolution R Enterprise ScaleR’s algorithms are available on each supported platform
  • Consistency:  Behavior of Revolution R Enterprise ScaleR algorithms is consistent from platform to platform despite big differences in capability and architecture.
  • Performance:  Revolution R Enterprise DistributedR assures that Revolution R Enterprise ScaleR algorithms run efficiently in all environments using optimum resource management, communications and I/O.

DistributedR Simplifies Portability of R Analytics

Revolution R Enterprise DistributedR brings portability to ScaleR algorithms. By changing just one simple line in an R script, a user can direct the execution of R analytics to run on any supported Revolution R Enterprise platform.

Support for EDWs and Hadoop Clusters

With versions for Teradata Database and multiple distributions of Hadoop, DistributedR maximizes the range of data scale and compute performance available to R developers through support of popular Big Data platforms.

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