R?is the most widely used programming language in the world of data science and heavily used for statistical modelling and predictive analytics. The popularity of R is driving many commercial big data and analytics providers to not only provide first class support for R, but also create software and services around R. Microsoft is not far behind. Months after its acquisition of Revolution Analytics, the company leading the commercial software and services development around R, Microsoft is now ready with R Server. Microsoft R Server?is an enterprise scale analytics platform supporting a range of machine learning capabilities based on the R language. It supports all stages of analytics viz. explore, analyse, model and visualize. It can run R scripts and CRAN packages.
In addition, it overcomes the limitations of R open source by supporting parallel processing, thereby allowing a multi-fold increase in the analytical capabilities. Microsoft R Server has support for Hadoop, thereby allowing developers to distribute processing of R data models across Hadoop clusters. It also has support for Teradata. Interests on cloud are also taken care. The Data Science Virtual Machine?will now come pre-built with R Server Developer Edition. You can now leverage the scale of Azure to run your R data models. For Windows, R Server ships as R services in SQL Server 2016. While currently in CTP you can install the advanced analytics extensions during the installation of SQL Server 2016 to use a new service called the SQL Server Launchpad and integrate with Microsoft R Open using standard T-SQL statements. To enable R integration then, you can run the sp_configure?command and give permissions to a user to run R scripts:
sp_configure 'external scripts enabled', 1reconfigureGOalter role db_rrerole add member [name]; You can then connect using your IDE like R Studio?to develop and run R code. Microsoft will also shortly launch R tools for Visual Studio (RTVS), and you will be able to run R from within Visual Studio.
With enterprises embracing R and providing solutions for commercial use, it is only a matter of time before developers fully embrace this language for enterprise scale data analysis.
