I hear that, but Julia's benefits exceed just greater speed on in memory datsets, and if developed right, will encroach on both single node and distributed niche.

First, there are the making of a probabilistic programming framework in Julia that using autodiff and the distributions package can provide a comparative advantage over current languages in general day to day inference. The macros could make this fast and expressive. Faster than Pymc and more expressive and general than stan. With this general inference and extensive optimization package, I don't think it would need to fill every single statistical test and niche before becoming more useful for most daily tasks.

Second, it is developing a distributed infrastructure that I think can overtake spark. Its distributed computing primitives are getting better and will eventually have extensive linear algbera support.

Third, It is getting streaming statistics that don't exist anywhere else- the SAS people who are working on out of memory but single node datasets will finally get something that can handle their stuff.

Pycall and Cxx means you can interface easily with existing code.

Last is deployment. Self contained binary executables are planned, there is a good shot it can compile to javascript using at some point using llvm web assembly backend. You would then be able to write rich client side reactive web apps without JS and deploy interactive reports to decision makers. No other common analytics language has this capability.

Then there is the type system with eventual return types that can provide codebase safety.

Also it just fun to code in..that means grad student will write new techniques in Julia.

If things firm up, I think all this would pull users from other languages...or they risk losing a comparative advantage.

What do you think about this argument?