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.
- 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.