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Connect to an S3 bucket directly in your notebook

Mount an AWS S3 bucket into your notebook and browse files just like do on your computer. You can read, write, update or delete any data.

Read the Amazon S3 docs
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Amazon S3 Buckets in Jupyter notebooks

With Amazon S3 you can easily store any object in the cloud.

When connected to a Deepnote notebook, the bucket will be mounted along with the notebook's filesystem. Then you can easily reference, upload, delete or update any file that lives in the bucket. S3 can be used to store large datasets that will serve as inputs to training or analysis, or you can directly save there the outputs of your work.

Explore Amazon S3 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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