In general, on the surface, it is to some extent quite similar in Julia as well, though not as pronounced as in other ecosystems. Depending on how you look at it, one could even argue that there is a lot of C++ in Julia because of LLVM. However, I don’t think that claim is fully justified, since, at least as far as I know, thanks to this high-level assembly, much can be expressed directly in Julia’s syntax. Anyway, for example, the upcoming comp neuro & machine learning framework built on Enzyme.jl, Reactant.jl, and Lux.jl relies at its lower levels on LLVM IR and C++, at the middle level mainly on MLIR, and at the higher level primarily on Julia.
To answer your question: honestly, I think it may be more rewarding to focus on innovation than on supporting other ecosystems, especially given that Julia, like Python, is a general purpose language. That said, although I have recently become again more optimistic about Julia (with developments such as PackageCompiler.jl, JuliaC.jl, BorrowChecker.jl, Enzyme.jl, Reactant.jl, Lux.jl, and ongoing research on the GC), I believe there remains a significant risk, mainly due to the speed at which other ecosystems are advancing.
Personally, I am committed to Julia while also experimenting with q/kdb-x. I believe Julia is particularly well suited for time series analysis. By the way, could you share more information about the Time Series Analysis & Forecasting Society and its activities? And would you consider presenting Durbyn.jl at one of the JuliaDynamics monthly meetings? Thanks again for the package!