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How Cube's Universal Semantic Layer & Databricks Lakehouse Work Together
Build a Databricks API with Cube.
How Cube and Databricks work together
Integrate Cube with Databricks and your data apps to get semantic modeling, data access control, and caching—for consistent metrics definitions and access control management across every application and data consumer.
Power your data applications with Databricks and Cube
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.
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.
Performance + Reliability
Redundantly and directly querying your data source results in latency and unnecessary costs. Cube’s caching layer, pre-aggregations, and query queuing fix that.
Skip latency;
expect speed.
Additional information on using Cube with Databricks
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
