Tutorial
Deepnote tutorial: Collaborative data analysis in a Jupyter-compatible data notebook
Build a 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.
Step-by-step guides on using Cube with Jupyter
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
Jc Weinrich
Architect in Jobber
Interest piqued? Try Cube's semantic layer.
Fully managed hosting of your Cube apps
