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When SQL Server receives an R-scrip for execution, it launches the Advanced Analytics Extensions Launchpad process that enables integration with Microsoft R Open using standard T-SQL statements.Then the R-script is handed over to R Interpreter for execution and the result is returned to the client.The example below passes a dataset or result-set from SQL Server to R-scripts, then inside the R-script execution, summary statistics are being calculated. State Province Name;' In the earlier example, we used the @input_data_1 parameter to specify a query to pass a dataset from SQL Server to R-script. State Province Name;' Earlier examples show how you can pass a dataset back and forth between SQL Server and R runtime during script execution.
This becomes mandatory if you use @output_data_1_name clause to return a data-frame from R script unless you use the INSERT …EXEC statement to write output to a table.
There are multiple ways that data scientists can write and execute R scripts for developing and building predictive models.
Based on preference, they can use any R client tools.
Therefore, it’s now becoming necessary for every organization to exploit the data that they have (or in some cases external data as well) and predict future outcomes to remain competitive by offering their customers what they want, when they want, and where they want.
Looking at this potential, SQL Server 2016 brings native support for doing advanced analytics in the database itself using R Services, without moving data across.All SQLServer service packs are cumulative, meaning that each new service pack contains all the fixes that are included with previous service packs and any new fixes. If you know of a hotfix build or KB that we don't have listed here, please use the comments.