You post seemed to argue that Dask is fine is you stick to relatively small data that fits on a single node then switch to pandas. You also noted that this is what Dask recommends. Implication being that I ran into issues because I didn't use Dask "the right way."
I don't see how you can argue both that and that dask distributed is production ready at the same time.
I've been in big data for 15 years and was probably one of the first few thousand production hadoop users. If you think "a big company used a big data tech so it's production ready" is an argument then I've got a few bridges to sell you. A lot of companies use a lot of technologies that they spend a lot of time beating into a shape where for their specific use cases they work just well enough to not get them all fired.
I don't see how you can argue both that and that dask distributed is production ready at the same time.
I've been in big data for 15 years and was probably one of the first few thousand production hadoop users. If you think "a big company used a big data tech so it's production ready" is an argument then I've got a few bridges to sell you. A lot of companies use a lot of technologies that they spend a lot of time beating into a shape where for their specific use cases they work just well enough to not get them all fired.