
Bridging analytics and data science workflows with GPUs - tmostak
https://www.omnisci.com/blog/announcing-omnisci-4.8
======
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/](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](https://observablehq.com/search?query=mapd)
and
[https://observablehq.com/search?query=omnisci](https://observablehq.com/search?query=omnisci).
But imagine we could do something deeper, any thoughts?

~~~
jrajav
Mike Bostock's notebook here is a great starting point, showing how to import
and use mapd-connector [1] in an Observable notebook and then feed its data
into a vega-lite chart:

[https://observablehq.com/@mbostock/hello-mapd-
connector](https://observablehq.com/@mbostock/hello-mapd-connector)

[1]: The Javascript client for connecting to OmniSci,
[https://github.com/omnisci/mapd-connector](https://github.com/omnisci/mapd-
connector)

------
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=...](https://twitter.com/ToddMostak/status/1162067442081751040?s=20)
With a GPU cluster it's very possible to handle 100+ billion rows with sub-
second response times.

