Tutorial
Introducing Cube Store: High concurrency and sub-second latency for any database
Build an Elasticsearch API with Cube.
How Cube and Elasticsearch work together
Use Cube with Elasticsearch to make data modeling, centralized access control, and caching available to downstream applications powered by key-value or document-based data.
Power your data applications with Elasticsearch and Cube
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.
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.
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 Elasticsearch
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
