Build a Jupyter Semantic Layer with Cube.

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How Cube and Jupyter work together

Cube’s ‘headlessness’ means you can build custom visualizations with Jupyter that are based on consistent metrics, governed by centralized permissions, and made performant by caching. With Cube and Jupyter, ensure everyone is working with consistently defined, governed, and performant data.

Power your data applications with Jupyter and Cube

Headless = Customized

Decoupling BI from your front-end means orchestrating data modeling, access control, and caching once—while building presentation layers for any use case.

Easily tailor your
user experience.

Security + Governance

Reconfiguring data access control for every data app in your stack opens the door to inconsistencies and gaps. Centralize permissions upstream for uniform security.

Maintain ubiquitious
access control.

Compatibility + Flexibility

Cube’s API-first approach and SQL-compliant protocol enable you to maintain every BI tool or data app with consistent metrics definitions, access control, and caching.

Integrate with anything
and anyone.

Step-by-step guides on using Cube with Jupyter

See Cube in action

Interest piqued? Try Cube's semantic layer.

With Cube, we’ve been able to speed up time to release a new data model to production by 5x and decrease analytics downtime by 90%.

Picture of Alessandro Lollo - the Participant

Alessandro Lollo

Senior Data Engineer at Cloud Academy

Related Integrations

Check out Cube’s other integrations

Related Use Cases

Check out Cube’s other Use Cases

Semantic Layer

Define metrics upstream to inform every app with the same data.