Work faster by allowing anyone to add comments, questions, and feedback.
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.
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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.
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 →