[Kranar]

I think your question presupposes a lot of assumptions that may not be right. For one, I don't know that Julia is like a "big deal", certainly Python is the big deal in this field and I doubt Julia is looking to displace it wholesale. That said, Julia is a great addition to the scientific computing landscape because of its performance compared to other languages and its use of modern programming features. Python is just really really slow compared to Julia and parallelism in Python is a huge pain. Fortran is really really fast but that comes at a cost of being awkward to use and coming with a great deal of baggage. Julia is fast, feels modern, and has pretty easy parallelism.

Then there's Matlab, Mathematica, and they are also pretty good but they're closed source/proprietary, so their ecosystem is mostly limited and driven by commercial interests. Nothing wrong with that intrinsically and they're all widely used but it's one way Julia differentiates itself, by making the language open and making money through services.

[ChrisRackauckas]

Julia Computing is not a services company. There are commercial products built off of this stack which are the core of Julia Computing. For example, https://pumas.ai/ is a product for pharmacology modeling and simulation, and runs on the JuliaHub cloud platform of Julia Computing. It is already a big deal in the industry, with the quote everyone refers to "Pumas has emerged as our 'go-to' tool for most of our analyses in recent months" from the Director Head of Clinical Pharmacology and Pharmacometrics at Moderna Therepeutics during 2020 (for details, see the full approved press release from the Pumas.ai website). JuliaSim is another major product which is being released soon, along with JuliaSPICE publicly in the pipeline.

But indeed, Julia Computing differentiates itself from something like MATLAB or Mathematica by leveraging a strong open source community on which these products are developed. These products add a lot of the details that are generally lacking in the open source space, such as strong adherents to file formats, regulatory compliance and validation, GUIs, etc. which are required to take such a product from "this guy can use it" to a fully marketable product usable by non-computational scientists. I will elaborate a bit more on this at JuliaCon next week in my talk on the release of JuliaSim.

[codekilla]

Wanted to ask if JuliaDB is something that might get more development attention? Or will that remain a community project? (I see it’s been in need of a release for awhile.)

[ChrisRackauckas]

It is not in our current set of major products. That said, informal office discussions mentioned JuliaDB as recently as last week, so it's not forgotten. If there's a demonstrated market, say a need for new high-performance cloud data science tools as part of the pharmaceutical domains we work in, then something like JuliaDB could possibly be revived in the future (of course, this is no guarantee).

[ViralBShah]

In general, the community has discussed reviving the project (or at least the ideas and some of its codebase). Julia computing will also be contributing as part of that revival.

[codekilla]

Thank you both for the comments. I believe I remember early on there were some comparisons to kdb+/q. I think there is some pretty great potential with an offering like this (an in-memory database integrated with the language, coupled with solid static storage) from the Julia community going forward. I can envision some use cases in genomics/transcriptomics.