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Use SQL to query Snowflake from a notebook

Don’t jump between multiple apps. Query data directly from your Snowflake data warehouse. Switch between SQL and Python in order to transform, clean, and export your data.

Read the Snowflake docs
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An exploratory analysis in Deepnote notebook

Snowflake in Jupyter notebooks

Snowflake is an enterprise-ready data warehouse that lets you separate your compute from storage.

When connected to a Deepnote notebook, you can read, update or delete any data directly with Snowflake SQL queries. The query result can be saved as a dataframe and later analyzed or transformed in Python, or plotted with Deepnote's visualization cells without writing any code.

Explore Snowflake docs →
Snowflake, MongoDB, PostgreSQL and an Amazon S3 bucket connected to a Deepnote project as integrations

Collaborative by default

We built collaboration into Deepnote by default because data teams don’t work alone.

Deepnote runs seamlessly in the cloud, making environment management a non-issue. And sharing work is as easy as sending a link (think Google Docs).


Allie Russel

Allie Russell · Senior Manager, Data Science at Webflow

“Deepnote allowed us get on the same page through collaboration, and everyone gets to use their preferred tools.”

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Team of 4 collaborators with different permissions for greater data security

Integrates with your data stack

Deepnote works with the tools and frameworks you’re already using and familiar with. Use Python, SQL, R, TensorFlow, PyTorch, and any of your favorite languages or frameworks. Easily connect to data sources with dozens of native integrations.


Becca Carter

Becca Carter · Analytics Lead at Gusto

”Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds.”

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