Starting with SQL Server 2016, R integration became a very important part of the SQL Server platform. Since the public release of SQL server 2016, until February 2018 (the time of writing this), the community had embraced R as well as Python very well, making data exploration and data analysis part of the general database task. Microsoft addressed many of the issues, and broadened the SQL Server as a product. With SQL Server 2017, Python was added as a secondary analytical language, reaching to an even broader community as well as businesses, and at the same time, taking are of data scalability, performance, and security.
In the next chapter, we will cover different R distributions and IDE tools for using R as a standalone or within the SQL Server, and what the differences among them are when deciding which one to choose.
