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Query MongoDB directly from a notebook

Deepnote securely stores the credentials to your MongoDB instance so you can use pymongo to retrieve, update or delete any data on your MongoDB instance.

Read the MongoDB docs
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An exploratory analysis in Deepnote notebook

MongoDB in Jupyter notebooks

MongoDB is a document database, that lets you store data in JSON-like documents.

With Deepnote's MongoDB notebook integration, you can query any object from your database directly in Python. In addition to querying for a single or multiple documents, you can also insert, update or delete any JSON document. With Deepnote, you don't need to add an extra layer between your MongoDB data and resulting analysis.

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

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. And sharing work is as easy as sending a link (think Google Docs).


Allie Russel

Allie Russell · Senior Manager, Data Science at Webflow

“Deepnote allowed us get on the same page through collaboration, and everyone gets to use their preferred tools.”

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Team of 4 collaborators with different permissions for greater data security

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.


Becca Carter

Becca Carter · Analytics Lead at Gusto

”Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds.”

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