Work faster by allowing anyone to add comments, questions, and feedback.
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.
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).
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.
Browse integrations →