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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.

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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.

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

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

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.

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