Bridging analytics and data science workflows with GPUs

[josep2]

A few comments:

1. I'm usually down on dark-mode but this looks clean as hell 2. The JupyterHub integration looks nice! It was mentioned in the article it was the "tip of the iceberg" I'm wondering how hard it would be to integrate with a tool like Observable.

Observable: https://observablehq.com/

[tmostak]

Thanks! We're big Observable fans, and you can find quite a few examples here: https://observablehq.com/search?query=mapd and https://observablehq.com/search?query=omnisci. But imagine we could do something deeper, any thoughts?

[randyzwitch]

It's certainly possible, I guess the bigger question is whether people would be comfortable having a database do an automatic data transfer into a public tool. In the case of this Jupyter integration, it's a Docker image that's running next to the OmniSci database, so it's all within the same network

[jonbaer]

The support for JupyterLab looks great and +1 for Dark Mode.

[tmostak]

Thanks @jonbaer, and lot's more exciting stuff to come!

[usujason]

Dark Mode looks amazing. So clean.

I'm looking forward to exploring the integration with Jupyter.

[emplynx]

Does it scale?

[tmostak]

Check out this example crunching 1.45 billion rows on my 32GB Macbook Pro (CPU-only). https://twitter.com/ToddMostak/status/1162067442081751040?s=... With a GPU cluster it's very possible to handle 100+ billion rows with sub-second response times.

[randyzwitch]

Could you be more specific? Does which part scale?