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.Trusted by data scientists at
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 direclty save there the outputs of your work.
We built Deepnote because analysts and data scientists don’t work alone.
Click on a link to jump into any project and see in real-time what everyone is up to.
No more out-of-sync files.
“Working in Deepnote is like code-review and rapid prototyping at the same time, saving valuable time in the iteration cycles.”
No need to email files or take screenshots of charts. In Deepnote, you can share projects by sending a link to anyone. By setting team permissions, you decide who can edit code and who can view it.
Discuss and debug in real-time by commenting on code and visualizations. No more email or Slack messages to get feedback on your work.
Are you looking at the latest version of your file? See all changes as they happen when you are working with colleagues or clients.
Deepnote integrates flawlessly with all your existing infrastructure and processes.
Use Python, R, Julia, TensorFlow, PyTorch, or any of your favorite languages or frameworks.
We’ve also pre-built 100s of native integrations that simplify the process of connecting your data sources.
All integrations are encrypted and can be easily shared with your team. No exposed passwords in your notebooks.
Attach a repository to your project to read and commit to any branch.
Mount Google Drive directories in your projects to read and edit its files.
Connect to your BigQuery warehouse and query the data with dedicated SQL cells.
Connect to a Snowflake warehouse and query the data with dedicated SQL cells.
Attach a bucket to your project to read, edit and upload files to the bucket.
Connect to a Postgres instance and use SQL directly from a notebook inerface.