I have in my hands a pretty interesting BI project for a big company. So far, the proposal on the table has been .NET and SQL Server, but I am wondering if I should at least try to give python a chance. Pandas is a great library, with great people working on it. Django the same. On the other hand, .NET has lots of professional (aka: with paid licenses) libraries that seem more fit for an enterprise project. Looking from a company perspective, the drawback python has is, strangely, the lack of paid for alternatives. It's not that people in companies don't trust open source (hadoop is becoming big here too), but one wonders if the developers will be able to find the support they need in case any issue arise from a free library.
I have first-hand experience with a BI-ish system, squarely targeted at the enterprise and doing quite well there, that we wrote using Django and a whole list of open source components.
We did run into some resistance initially, because our stack is almost the opposite in every way of what our enterprise colleagues are used to. However, our development velocity, especially around analytical features and just in general, has made a most gratifying impact.
(I have to add that the 5-man dev team we have working on this is stellar. It's hard to determine scientifically what the interaction is between team quality and the choice of a Python-oriented software stack. See Paul Graham's essays for more discussion on that point.)
In terms of support: There are many highly professional often boutique software agencies that can support Django systems, if you're not around. To my mind this is even better than the normal commercial support you get from a different vendor for each different component in your commercial enterprise system.
It would be interesting to hear about your experience, did you do a write up somewhere or could I email you with few questions?
My project would be to put different data sources together + to allow users to upload their own structured data via Excel (think financial estimates). The current system has about 450 users, the next might have much more depending if it gets extended to other divisions.
one wonders if the developers will be able to find the support they need in case any issue arise from a free library.
Continuum Analytics is founded by the creator of Numpy and employs many leading python developers, and they offer support contracts for basically the entire Python/Numpy data analysis stack.
I would like to second the suggestion for F#. It has a nice balance in that it straddles the line between being an OSS ecosystem and an enterprise ecosystem with paid libraries. FsLab and Deedle got mentioned already but the F# website has a lot of great resources too: http://fsharp.org/guides/data-science/index.html
