Sign inGet started

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.

Get started – it’s freeRead the Amazon S3 docs

Loved by 100,000s of data professionals

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 Buckets in Jupyter notebooks docs →
Snowflake, MongoDB, PostgreSQL and an Amazon S3 bucket connected to a Deepnote project as integrations

Collaborate with the whole team

Deepnote runs seamlessly in the cloud, making environment management and collaboration with your team a non-issue. And sharing work is as easy as sending a link or email invite.

Product manager

Organize your work

Build a library of data projects sorted by folders so teammates can get needed information fast.

Comment, review, version

Have your team comment on blocks to ask questions, provide feedback, and work faster.

Data scientist

Sharing made simple

Share your work with others by simply sending a link or email invite. Or use advanced permission models.

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 →

Datastores and Metrics





  • Integrations
  • Pricing
  • Documentation
  • Changelog
  • Security




  • Privacy
  • Terms

© Deepnote