Revolution Analytics Brings Big Data Analysis to R

R language can now tackle terabyte-class data sets using Revolution R Enterprise-at a fraction of the cost of legacy analytics products

The world's most powerful statistics language can now tackle terabyte-class data sets using Revolution R Enterprise-at a fraction of the cost of legacy analytics products

JSM 2010 - VANCOUVER (August 3, 2010) - Revolution Analytics today introduced 'Big Data' analysis to its Revolution R Enterprise software, taking the popular R statistics language to unprecedented new levels of capacity and performance for analyzing very large data sets. For the first time, R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products-without employing expensive or specialized hardware.

The new version of Revolution R Enterprise introduces an add-on package called RevoScaleR that provides a new framework for fast and efficient multi-core processing of large data sets. It includes:

The XDF file format, a new binary 'Big Data' file format with an interface to the R language that provides high-speed access to arbitrary rows, blocks and columns of data.
A collection of widely-used statistical algorithms optimized for Big Data, including high-performance implementations of Summary Statistics, Linear Regression, Binomial Logistic Regression and Crosstabs-with more to be added in the near future.
Data Reading & Transformation tools that allow users to interactively explore and prepare large data sets for analysis.
Extensibility: expert R users can develop and extend their own statistical algorithms to take advantage of Revolution R Enterprise's new speed and scalability capabilities.

"The R language's inherent power and extensibility has driven its explosive adoption as the modern system for predictive analytics," said Norman H. Nie, president and CEO of Revolution Analytics. "We believe that this new Big Data scalability will help R transition from an amazing research and prototyping tool to a production-ready platform for enterprise applications such as quantitative finance and risk management, social media, bioinformatics and telecommunications data analysis."

Read more.

See also Revolution Analytics targets R language, platform at growing need to handle 'big data' crunching challenges, analysis By Dana Gardner, ZDnet blog.

"With RevoScaleR, we've focused on making analytical models not just scale to the big data sets, but run the analysis in a fraction of the time compared to traditional systems," says David Smith, vice president of Community and Marketing at Revolution Analytics. "For example, the FAA publishes a data set that contains every commercial airline take off and landing between 1987 and 2008. That's more than 13 gigabytes of data. By analyzing that data, we can figure out the likelihood of airline delays in one second."