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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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An exploratory analysis in Deepnote notebook

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

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