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.

Data Modeling

Define and manage metrics upstream to consolidate your workflow, centralize definitions, and create a single source of truth.

Access Control

Grant column- and role-based operational and viewing permissions upstream with granular access controls.

Caching

Ensure your data is reliably performant with a powerful caching layer and advanced preaggregation capabilities.

APIs

Connect your data to any front-end application to build beautiful custom visualizations with Cube’s GraphQL, REST, and SQL APIs.

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

Tutorial

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

Check the issues

250 contributors and 14.1k stars on GitHub

Join our Slack to ask questions

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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

Read the Case Study

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