Work faster by allowing anyone to add comments, questions, and feedback.
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.
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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.
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. Sharing work is as easy as sending a link (think Google Docs).
Build a library of data projects sorted by folders so teammates can get needed information fast.
Share your work with stakeholders by simply sending a link or email invite.
Collaborative commenting
Work faster by allowing anyone to add comments, questions, and feedback.
Organize with ease
Build a library of data projects sorted by folders so teammates can get needed information fast.
Sharing made simple
Share your work with stakeholders by simply sending a link or email invite.
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.
Browse integrations →