The R Analytics Programming Language Comes to Big Data

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It was really only a matter of time before the industry saw some convergence between the open source R programming language that is gaining popularity in data analytics circles and Big Data. After all, the whole point of collecting all that data is finding some way to analyze it.

Unfortunately, many organizations don't have the expertise required to master statistical analytic offerings such as SAS or SPPS from IBM. As a result, interest in R as an inexpensive alternative has increased dramatically in the last year, resulting in companies such as Revolution Analytics bringing an enterprise-class implementation of R to market. Now Revolution Analytics is partnering with the Netezza unit of IBM to marry R to a high-speed data warehouse appliance for analyzing Big Data formats such as Hadoop.

According to Revolution Analytics COO Jeff Erhardt, as customers work more with R they are encountering more instances where they need analytic results quickly. So while R provides a framework for analyzing the data, those customers still need a platform that can return results quickly. The IBM-Netezza alliance is the first in a series of alliances that Revolution Analytics expects to make with similar synergies in mind, said Erhardt.

There obviously is a significant restructuring of how data is being analyzed and processed taking place in the enterprise. That movement may still be relatively nascent, but Matthew Rollender, director of technology and strategic alliances for IBM Netezza, points out that both R and Hadoop are making significant inroads in the financial services and pharmaceutical industries. That means that at this point it's only a matter of time before we see further mainstream adoption of both R and Hadoop. The only thing that isn't known is how many IT organizations will think to combine these two technologies to create something that truly is more powerful than the sum of its parts.