I'm still not sold on Julia. Compared to R/python, the supposed benefit doesn't outweigh the cost of implementation and learning (my perception). I do a lot of "big data" stuff in R/python, and I just use libaries written in C or C++.
Really the premise of Julia is to make their life easier, while leaving yours equivalently easy (or better).
But this introspection gave me a reality check. In my own work, as much as I love Julia, I fallback to quickly using Python libs. In industry, most of the people's focus is on using someone's already written code. The byproduct is inheriting Python and C/C++ interface mess around dependency management. People needing to write their own algorithms are majority academics and researchers where Julia has flourished.
The incentives of corporate work are setup to build on top of Python's ecosystem which means (sadly) Julia will stay in its niche.
Julia’s proposition is that the entire community should be able to read and contribute to these libraries. Unlike the python community where the vast majority of users could not read the source behind those libraries let alone contribute.
A fresher take on scientific computing like Julia, if it is mainstream, might enable more contributions and in general understandability of the black box algorithms.