Revolution Analytics Extends R Programming Integration

More analytics applications than ever are being developed using the open source R programming language. This means integrating these applications with all the other applications that need to consume that information has become something of a challenge. With the release of version 7.0 of its commercial implementation of an R programming environment, Revolution Analytics is trying to simplify that process.

Revolution R Enterprise 7 adds “write-once, deploy-anywhere” functionality that allows a predictive analytics application written in R to run across, for example, Hadoop and a Teradata database.

In addition, the new release adds integration accelerators for Tableau data visualization software and Excel spreadsheets, improved ODBC connectivity and predictive modeling markup language (PMML) export.

A new generation of data scientists are using R to create analytics applications, but those applications are not easily integrated with other applications, says David Smith, Revolution Analytics' vice president of corporate marketing and community. To facilitate that process, the enterprise edition of Revolution R allows data scientists to expose those applications via an API that masks any of the complexity associated with the R programming language.

Going forward that’s going to be a critical capability because most analytics is going to be integrated with other applications to deliver insight at the point of consumption. Rather than having to fire up a separate analytics application, the analysis created by a predictive analytics application written in R will be embedded within the application that is being primarily used at any given moment.

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As a programming language, R has emerged in the last few years as a credible alternative to proprietary SAS and IBM SPSS application environments. At a time when interest in Big Data is at an all-time high and the number of data scientists is low, the R programming language is one of the primary mediums through which the ranks of data scientists will be expanded. As the number of college graduates with R programming skills and the number of analysts attaining proficiency in R increases, the number of analytics applications written in R should considerably expand in the years ahead.

The challenge will be finding ways to integrate all those applications with the myriad of applications that are written in a multitude of different programming languages. The good news is that as APIs become the standard way for achieving that integration, there will be a wealth of analytics in place, providing data that will be well worth the effort to actually consume.

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