Data-scientist who loves to use #datascienceforgood, especially in ecology, energy and the environment. Bonsai, gardening, bikes and music when I'm not at a keyboard.
I've been following some of the content floating around about dark. I've not really had a usecase to go into anything more than what you and Ellen have put up on blogs, but at the same time I liked the message.
I think I liked the message most because dark solved a friction I could see between langauge-editor-deployment which is solved in a parallel way for my core work, data science in R.
RStudio have in many ways built for data science what you are doing now.
Language - R - admittedly this predated RStudio at it's core, but arguably tidyverse has made the language almost entirely anew. You can tell when someone learnt R from their use of tidyverse, piping and non-standard evaluation. This is all open source and totally free.
IDE - RStudio - it's actually insane to see anyone use anything else now. Vscode in some niche issues, but tooling in extensions is pretty barebones. Enterprise licensing is available for the big corporates that put value in that.
Deployment - RConnect, Rpubs, Shiny Server - one click deploys for various outputs. This is the place that they monetise most heavily. Also therefore the most uncommon things to see people doing.
I'm not saying that this works for dark at all. I'm sure there are nuances, applications and use case differences in the vision that mix these up, but as a model, maybe it might be worth understanding?
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I've been following some of the content floating around about dark. I've not really had a usecase to go into anything more than what you and Ellen have put up on blogs, but at the same time I liked the message.
I think I liked the message most because dark solved a friction I could see between langauge-editor-deployment which is solved in a parallel way for my core work, data science in R.
RStudio have in many ways built for data science what you are doing now.
I'm not saying that this works for dark at all. I'm sure there are nuances, applications and use case differences in the vision that mix these up, but as a model, maybe it might be worth understanding?