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Comment by OldTimeCoffee | Hacker News Reader

Most users don't have 'tens-thousands' of GB of data as part of their use case. You're describing a business case, not an end user consumer case.

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I don’t believe that the majority of Jupyterhub users are “consumers” rather than professionals, but more importantly, that doesn’t change the fact that the professional use case exist and isn’t amenable to the fat client approach.

As an anecdote, I work on a Jupyterhub managed service offering with customers in both the private and public sectors and our data sizes are pretty much all in this range.

> I work on a Jupyterhub managed service offering ...

That sounds like selection bias.

It might be. Like I said: anecdote.

Isn't it, though? The people most likely to need a cloud-managed service like that probably have too much data to crunch on a laptop, as you described.

Ah, to be clear I’m not saying our users are a representative sample of Jupyterhub users. I am saying that there are a lot of people who use Jupyterhub for large datasets—it’s certainly not uncommon.

Most of the users will not run Jupyter Notebooks locally, they expect a remote kernel in the cloud with GPU/multicore machines, only the web app will be running in browser (think of big query). Of course small users with you datasets will run locally.

This depends greatly what you want to do. After all, this has incredible latency (compared to the cloud), limited throughput. Zero configuration. Free.

You want to train the next imagenet model? Analyse 100Gig database? Probably not the correct tradeoff.

You want 20 students to have a perfectly consistent instant-start-up python environment? Definitely the correct tradeoff.

You want to try some python methods, write tests, ... Very short latency is going to help you more than throughput is.

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