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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 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 direclty save there the outputs of your work.

Explore S3 docs →
Snowflake, MongoDB, PostgreSQL and an Amazon S3 bucket connected to a Deepnote project as integrations

Move faster with collaboration

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.”

Luca Naef

Luca Naef

CTO at VantAI VantAI
Read VantAI case study →
Team of 4 collaborators with different permissions for greater data security

Share with your team, your clients, or the world

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.

Team of 2 collaborators using Deepnote comments in order to communicate and work together directly within a data science notebook

Review work in the right context

Discuss and debug in real-time by commenting on code and visualizations. No more email or Slack messages to get feedback on your work.

Deepnote showing the history of changes made by data scientists in a team to a notebook

See history, track changes

Are you looking at the latest version of your file? See all changes as they happen when you are working with colleagues or clients.

Integrates with everything

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.

Show all integrations →

GitHub

Attach a repository to your project to read and commit to any branch.

Google Drive

Mount Google Drive directories in your projects to read and edit its files.

BigQuery

Connect to your BigQuery warehouse and query the data with dedicated SQL cells.

Snowflake

Connect to a Snowflake warehouse and query the data with dedicated SQL cells.

S3 Buckets

Attach a bucket to your project to read, edit and upload files to the bucket.

PostgreSQL

Connect to a Postgres instance and use SQL directly from a notebook inerface.

Deepnote has a growing list of integrations. Show all integrations →