We're using it in a soft-realtime setting to monitor industrial chlorine production, and for us it has been a very pleasant experience overall. Yes, we've had some issues, but similar to other ecosystem IMO, and our support contract with Julia computing helped us in the one case we really couldn't solve ourselves.
Julia works really well for power users. There are no huge libraries full of C code like pandas or scipy. Instead there are dozens of small, well-tested packages that fill the same role, all hosted on github. That makes fixing issues so much easier.
Granted: outside of numerical/technical computing, the libraries can be lacking (eg. web development) compared to other languages. We're doing it anyway, but it's a more difficult decision.
For data, we're monitoring ~1000 time series per plant, at about 2 points/minute. Julia's speed is not necessary there, but it is critical for historical simulations of algorithms.
Julia works really well for power users. There are no huge libraries full of C code like pandas or scipy. Instead there are dozens of small, well-tested packages that fill the same role, all hosted on github. That makes fixing issues so much easier.
Granted: outside of numerical/technical computing, the libraries can be lacking (eg. web development) compared to other languages. We're doing it anyway, but it's a more difficult decision.