I think one of Julia's greatest indicators of long-term success is the variety of commercial users/companies from a diverse pool of technical domains/industries that are all excited, willing, and capable of contributing back to the ecosystem.

We had a BoF at this year's JuliaCon revolving around this topic [1] and are now planning the first Annual Industry Julia Users Contributhon as a result.

I especially think that well-configured Julia + K8s setups have the capacity to really tighten exploratory data science <-> operational data engineering feedback loops in industrial settings in a way that is much more ergonomic, generically useful, and portable/extensible than using pre-baked frameworks to achieve something similar. Julia-centric tooling for coarse-grained workflow orchestration, experiment tracking, data provenance, etc. would be nice, though I also think existing generic tools in this vein (e.g. Argo) could probably compose well too :)

A few different companies have nice in-house implementations of these kinds of setups, and Julia Computing is building a nice looking commercial product suite in this vein that I look forward to exploring more in the future (especially JuliaHub and JuliaRun).

[1] https://julialang.org/blog/2020/09/juliacon-2020-open-source...