There are a number of different SQL Flavors–HQL, PL/SQL, MySQL, U-SQL, T-SQL — all of which are a derivative of Ansi-SQL, which is I suppose in today’s parlance, A-SQL. Many people have not heard of U-SQL, which Microsoft introduced on September 28, 2015. Since the announcement was in the Visual Studio Blog, a number of data people may have missed it. U-SQL is meant to combine the ease SQL with the functionality of C# to create a language which can process any kind of data, like videos or text, by creating the ability to customize the code and infinitely scale. This is very useful if for example all of the data is stored in an Azure Data Lake.
Using U-SQL in Azure Data Lake Analytics
In my previous series on Stream Analytics, I wrote some U-SQL. That U-SQL didn’t look much different than Ansi-SQL, which is sort of the point of porting the functionality to a different yet familiar language. Another application which heavily uses U-SQL is Azure Data Lake. Data Lake stores its data in HDInsight, but you don’t need to write hive to query the data, as U-SQL will do it. Like Hive, U-SQL can be used to create a schema on top of some data, and then query it.
For example, to write a query on this csv file stored in a Data Lake, I would need to create the data definition for the data, then I could easily write a statement to query it.
@testthis = SELECT SaleLocation
, COUNT(*) AS LocationCount
GROUP BY SaleLocation;
In this U-SQL code, I am creating a structure for the data, querying some fields, and writing the output to another file. Make sure that you don’t forget the semi-colons as that will cause errors. Also if any of your fields are blank you will have to code for that as well. From with Data Lake Analytics, the U-SQL is run as a job, creating a new file. Note the time that it took to finish the job.
The reason data is stored in a Data Lake is to provide a single storage location for the data, which will be used in analytics. U-SQL provides a powerful tool for getting the data out.
Data aficionado et SQL Raconteur