We use anaconda just as a python distribution on Windows. I think if you want a real data analytics setup which works well on all individual platforms then it's best to just build a docker-compose file. I was given a Dataiku presentation in New York in September and I liked it. My concern with such tools is that companies buy them thinking they are a 1 shop solution for analytics. For instance, how does Dataiku work with AWS/S3 Athena? Or how does Dataiku work with github? Maybe it works very well, I don't know, but evaluating those tools can take months. A "raw" python setup is, I think, an initial good first step as it forces teams to deal with all the architecture from the very beginning and it works because it integrates with everything. When you buy from a vendor you put yourself in a contract situation that could make it very hard to iterate to a final solution that actually works for the company. Open source doesn't have those problems.

If your organization's SAS experience is like where I just came from:

- 90% of your SAS usage is ETL via SAS/ACCESS -- easy to write, but no lineage or real reporting without costly engineering and maintenance. Current ETL tooling must more mature than was SAS offers

- 5% is actually using SAS for what it is intended, canned Statistical Packages with indemnification if their calculations are incorrect (watch out for SAS/STAT 9.3 time series, there are a few PROCs that have incorrect results!)

- 5% of your users are insanely frustrated trying to build real things on top of SAS's broken model of macros, PROC IML, procedure creators, and similar when they really just need Python.

Anaconda Enterprise is a good product, but really the FLOSS underneath just works well. Watch out for dependency hell (be disciplined by using virtualenvs / docker contains) and you'll see dramatic improvement in workflows.