I'm sure I'm not the only one who has read and contributed to threads on the internet about all the different languages used for data mining. But one aspect that's been left out of most of these comparisons is that SAS is more than a 4th generation programming language (4GL). It's always been and always will be more than a language because SAS has been engineered to be an analytic platform -- or to put it another way, an analytic processing environment designed to support the entire analytics life cycle.
What does this mean? It means that our software engineers have developed the environment to take advantage of underlying hardware such as CPUs, memory, etc., so that SAS users don't have to concern themselves with the details of leveraging the hardware efficiently. The SAS environment does it for them.
A good programmer may be able to use another language to create code that assists with efficiency, but that's complex coding that takes time, and not all programmers have that level of ability. In the long run, that can lead to inconsistencies in running and maintaining your business processes. SAS provides this type of efficiency either automatically or with a simple option setting.
It's like the choice between a potluck dinner at a friend's house versus eating out at a nice restaurant. Some of your friends will prepare better food than others, but few will provide the level of service and quality that a trained restaurant chef offers. In addition, good restaurants stand behind their food and service.
Why doesn't this topic come up more often? Probably because it's related to back-end architecture and not as easy to show off as a nice dashboard with the end results of the processing.
SAS provides either a Service Oriented Application (SOA) based architecture or a more modern cloud friendly micro-services based architecture (SAS Viya), or a combination of both, all engineered to make it easier for users to manage data, analyze it and deploy results in a consistent, governed and highly efficient manner -- regardless of the size of the data involved. If you're ready to learn more, make plans to attend Analytics Experience 2017, September 18-19 in Washington, D.C.