The SAS Data step is very roughly similar to one SQL statement or a sequence of SQL statements wrapped in a procedural language. It joins tables and subselects rows and columns based on some criteria and allows various mathematical operations on them.

Curt Monash posted a nice summary of the current and planned offerings that help to make SAS analytics more available “in the database” — allowing you to analyze your data quickly without having to move it around so much.

If you use SAS with Tera…

So, just scoring is a good first step… but I won’t consider the job done til I can build my models using the same parallel scalability.

Many if not most of Greenplum’s customers already access Greenplum using the SAS/ACCESS interface. Moreover, we are working directly with SAS to enable in-database scoring. It’s on our product roadmap for this year, and we are actively discussing this with them right now.

All this is obviously critical for us – there’s a huge number of SAS users out there who have a rich library of SAS code, and we need to make sure they can work seamlessly within our ‘data cloud’.

It’s important to point out that our philosophy is that analysts need access to a wide variety of different tools and applications for doing deep analytics. This includes SAS, of course, and BI tools, as well as R, MapReduce and so on. More power to the modelers! And that’s not to forget SQL of course. In many cases, complex models or MapReduce jobs can be done with surprisingly simple SQL statements. (I’m happy to share some with you if you like!) In other cases, not. You need different tools for different jobs. And that includes SAS.