Data catalogs and documenting data with Deepnote
A data catalog records an organization's data assets and the metadata that describes them. It helps users find a dataset, understand its source and schema, assess its quality, and check who can access it.
A catalog can include tables, charts, documents, images, machine learning models, and visualizations. Search, tags, filters, lineage, quality metrics, and access controls make those assets easier to find and evaluate.
Build a data catalog in Deepnote
Use Markdown blocks to document each dataset's source, creation date, schema, and purpose. Profile the data to find missing values, outliers, or inconsistencies, and add summary statistics or visualizations when they help readers understand the dataset.
Folders and tags can organize related datasets. Deepnote's version history records changes to the notebook, so your team can review updates to the catalog over time.
Share the project with teammates and set view or edit permissions for each collaborator. You can also publish the catalog as a Deepnote app and embed it in another document.
Example of data cataloging in Deepnote
The example data catalog notebook uses the Atlan API. Duplicate it to explore the setup or adapt it to your datasets.
Collaborating and sharing your data catalog
- Click Share in the top-right corner of the notebook.
- Set view or edit permissions for each collaborator.
- Share the link with your team, or create a data app from the catalog.
Collaborators can add, edit, or comment on the notebook. Use comments to discuss dataset details or flag issues.
Maintaining and updating your data catalog
Set a review schedule for the catalog. During each review, record changes to schemas, data sources, and descriptions. Use version history to inspect past updates or restore an earlier version.