Big Data Analysis with Revolution R Enterprise

Big Data Analysis with Open Source R

Revolution R Enterprise Breaks Through R’s Memory Barrier for Big Data Analysis

Revolution Analytics has taken the popular R language to unprecedented new levels of capacity and performance for statistical analysis of very large data sets. Using the built-in RevoScaleR package, 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.

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RevoScaleR: Big-Data Statistical Analysis with Revolution R Enterprise

Import “Big Data”: import your largest data sets from ASCII, SAS, SPSS, relational databases or data warehouses into R, without being constrained by memory limitations.

Powerful “Data Step”: Use the power of the R language to select records, transform variables, and sort and merge data. Thanks to scalable, out-of-memory parallel processing, there’s no need to leave the Revolution R environment to quickly prepare Big Data for analysis in R.

High-performance file-based analytics: the scalable, high-performance “XDF” data file format optimizes the process of streaming data from disk to memory, dramatically reducing the time needed for statistical analysis of large data sets. Multi-threaded statistical algorithms make use of all available processors to reduce computation time. Included are high-performance implementations of the following algorithms, with more planned for future updates:
  • Summary Statistics
  • Crosstabulations
  • Linear Regression
  • Binomial Logistic Regression
  • Correlation and Covariance
  • Principal Components Analysis
Analyzing Big Data from Airlines with R Linear regression and prediction on a 13GB, 120 million-row data set in under 60 seconds using a commodity dual-core PC. See how this analysis was created with Revolution R Enterprise with RevoScaleR in this video demonstration.


RevoScaleR: Distributed Computing with Windows HPC Server

Simple Distributed Computing: speed up processing even more by deploying the resources of a Windows HPC Server cluster — with just one additional line of code. See how you can calculate a logistic regression on a billion rows of data in under a minute in this video demonstration:



In-Database Analytics with Apache Hadoop and IBM Netezza

In-database R with Hadoop and IBM Netezza: if there’s more data than is practical to move, then bring Revolution R to your data for massively scalable analytics. Use the RevoConnectR for Hadoop packages# to write powerful map-reduce analytics using only the R language. Or use Revolution R Enterprise for IBM Netezza for in-database analytics using the power of a remote IBM Netezza data warehouse appliance.


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