The importance of R was first recognized by the SQL Server team back in 2016 with the launch of SQL ML Services and R Server. Over the years we have added Python to SQL ML Services in 2017 and Java support through our language extensions in 2019. Earlier this year we also announced the general availability of SQL ML Services into Azure SQL Managed Instance. SparkR, sparklyr, and PySpark are also available as part of SQL Server Big Data Clusters. We remain committed to R.
With that said, much has changed in the world of data science and analytics since 2016. Microsoft’s approach to open-source software has undergone a similar transformation in the same period. It is therefore time for us to share how we, in Azure SQL and SQL Server, are changing to meet the needs of our users and the R community moving forward.
I never used ML Server (but have used SQL Server ML Services a lot), so that part of the announcement doesn’t affect me and I’m not sure how many organizations it does affect. Switching to CRAN R is a good idea and I appreciate that they’re open-sourcing the RevoScaleR and revoscalepy code bases. The one thing I’d really like to see in vNext’s Machine Learning Services is an easy way to update the version of R