SQL Server 2016 R Services: introduction

This article is a short introduction to R programming language and a new R Services feature in SQL Server 2016. I hope, it will be a good starting point for those, who haven’t heard about it at all. R is a software environment and programming language for statistical computing, predictive analytics, and rich graphics. R can be easily extended through user-created packages, thousands of which are available in the CRAN (The Comprehensive R Archive Network), the central repository for all R binaries and third-party packages. Installing and updating packages from this source is an easy and straightforward process, which you can do just by one command or through graphical interface.

R is an open source project and is freely available for various operating systems, which is one of the main reasons why it has become substantially popular among statisticians and data scientists in recent years for data analysis, prototyping, and even software development. R community is huge and you can find an answer to almost any question or problem over the Internet, tons of scripts and examples are available for learning, downloading and modifying. Of course, such popularity has caused the inclusion of R language support to some Microsoft products and services.

On April 6th, 2015 Microsoft announced the acquisition of Revolution Analytics company, which has made R enterprise-ready by offering such products as R Open and R Server, which can scale and run R scripts number of times faster than an open source version by providing optimized versions of popular R built-in functions, multi-threaded processing and loading data to memory by chunks as needed.

Without a doubt, R Services in SQL Server 2016 is one of the biggest results of this unity. It brings R language support to one of the most popular RDBMS in the world, allowing to implement advanced analytics scenarios inside database engine and process large amounts of data using Revolution Analytics technologies. It’s possible to extend T-SQL code with more powerful R functions for data processing and predictive analytics. Moreover, you can now easily create rich graphics visualizations by using R capabilities and use these images inside your applications and reports.
In the next posts of this series, I will start to discover this new feature for you step by step and try to show its pros and cons, from the installation nuances up to processing large volumes of data in parallel. Hope, all this information will help you learn R Services and start using it.