alt Hacker NewsMy company (a hedge fund) has been using Julia for our major data/numeric pipelines for 4 years. It's been great. Very easy to translate math/algorithms into code, lots of syntactical niceties, parallelism/concurrency is easy, macros for the very rare cases you need them. It's easy to get high performance and possible to get extremely high performance.
It does have some well-known issues (like slow startup/compilation time) but if you're using it for long-running data pipelines it's great.
What kind of library stack do you use? Julia has lots of interesting niche libraries for online inference, e.g. Gen.jl, which can be quite relevant for a hedge fund.
If you can't talk about library stacks, it'd be at least interesting to hear your thoughts about how you minimize memory allocation.