SQL Server R Service – enterprise-scale data science


Data Science is the new workforce that has geared up in most of business domains now,data is a centric force for every organisation irrespective of size-of-business. In this space when Microsoft acquired Revolution Analytics that marks development tools and enhanced R services/packages, where few improvements have been made to integrate R with SQL Server family that enables users to run R code directly from TSQL. To know more about R see Installing SQL Server R Services  that contains client-server components and Enterprise/Open R services to configure.

 

R is popular programming language with open source community for advanced analytics to build required business models for better visibility, SQL Server 2016 CTP3 release is comprised of the following components:

  • Advanced Analytics Extensions: A framework for enabling R code to run in SQL Server, to support in-database analytics and much faster processing of R code.
  • Revolution R Enterprise 7.5.0. A collection of R libraries to support greater performance and scalability for R, including libraries for developing parallel algorithms, plus an optional R development environment with integrated tracing, Intellisense, and more.
  • Revolution R Open 3.2.2 for Revolution R Enterprise 7.5.0. A distribution of the open source R runtime and connectivity libraries that allow it to work with Revolution R Enterprise.

Also Checkout latest CTP3.3 release announcement as well.

To run R code with SQL Server just do the following (source: BOL):

  1. Open Management Studio and connect to the instance where you enabled Advanced Analytics Extensions.
  2. Open a new Query window and type sp_configure. If the extensions are enabled, the value of 'external scripts enabled' should be 1.

It is good to check few walkthroughs of these scenarios, see SQL Server R Services Tutorials

Not just with Enterprise or Standard editions, R integration support will be  enabled for SQL Server Express with Advanced Services as well.

There are few scenarios and examples where you can use R & SQL Server, see below: