Revolution Analytics to support in-Hadoop Big Data predictive analytics for Cloudera

Revolution Analytics will offer increased support for Hadoop as a platform for Big Data analytics with Cloudera CDH3 and CDH4 in its upcoming release of Revolution R Enterprise 7.0. With Revolution R Enterprise 7.0, the vast library of ScaleR algorithms will provide the easiest and fastest way to build and deploy R-powered Big Data analytics within Cloudera, eliminating data movement latency and speeding results.

“Hadoop has quickly evolved from a batch-oriented data store to a high-performance, integrated environment that allows organizations to process, visualize and search all kinds of data,” said Charles Zedlewski, vice president, Products, Cloudera. “With Revolution Analytics and the power of R, Cloudera customers will be able to easily build and deploy predictive analytics models. The convergence of R and Hadoop is a powerful advancement.”


Revolution Analytics’ partnership with Cloudera supports its commitment to provide enterprises with the flexibility to leverage R-enabled analytics with the data infrastructure platform of their choice. Revolution R Enterprise 6.2, currently available, is certified to work with Cloudera CDH3 and CDH4, allowing researchers to write their own Hadoop-based analytics in R and deploy them within the Cloudera environment. With Revolution R Enterprise 7.0, Cloudera customers will have the ability to quickly and easily invoke R-powered predictive models, and push beyond simple summaries, queries and data visualization to produce game-changing insights from data managed by the Hadoop environment. This can all be achieved without having to learn to write MapReduce in Java, Python or other languages, without using SQL and without having to know how to design parallel algorithms.
Cloudera customers will be able to take on Big Data analytics initiatives with Revolution R Enterprise. Revolution Analytics in-database analytics offers:
· Accelerated model development cycle times by eliminating data movement latency; and
· More complete, speedy results because the entire data set may be included in analysis at once, which is critical for any applications that detect outliers, such as to detect fraudulent claims or trades, or applications that score the entire data set, such as customer analytics, machine or sensor data analytics, or credit worthiness.


“Revolution Analytics is devoted to creating an ecosystem that connects existing data management technologies and platforms with the power of modern, R-based predictive analytics,” said Dave Rich, CEO of Revolution Analytics. “This new offering with Cloudera does just that: delivering customers the power, scale, economy and innovation they need to grow more quickly and work more efficiently.”
 

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