Jupyter Semantic Layer with Cube

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.

Additional information on using Cube with Jupyter

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Deepnote tutorial: Collaborative data analysis in a Jupyter-compatible data notebook

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See Cube in action

How Cloud Academy sped up data modeling by 5x and decreased analytics downtime by 90%

Read the Case Study

How Cuboh decreased 100,000-row customer report times from 10’s of seconds to less than 2

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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%.

Alessandro Lollo

Senior Data Engineer at Cloud Academy

We can develop and ship analytics products without dealing with any net new infrastructure development going forward. Our efforts are focused on what matters: the data and the UI. Read the Case Study

Louis Zuckerman

Sr. Staff Software Engineer at Simon Data

Cube really stood out as a great fit for our use case. We were able to level‑up our data infrastructure without needing to build a full‑blown and expensive data pipeline. Read the Case Study