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Cube is the universal semantic layer for modern data applications. Born in the cloud era, Cube represents the next evolution of OLAP technology, helping data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application.

Learn more about connecting Cube to data sources and analytics & visualization tools.

Cube was designed to work with all SQL-enabled data sources, including cloud data warehouses like Snowflake or Google BigQuery, query engines like Presto or Amazon Athena, and application databases like Postgres. Cube has a built-in relational caching engine to provide sub-second latency and high concurrency for API requests.

For more details, see the introduction page in our documentation.

As data infrastructure evolved from traditional relational databases to cloud data platforms, OLAP capabilities that once lived in specialized servers like SQL Server Analysis Services and Oracle Essbase were left behind. Today's organizations face several challenges:

1. Analytics Modeling and Multidimensionality. Modern cloud data platforms excel at processing large volumes of data but lack native support for multidimensional analysis and modeling. Cube brings OLAP-style analytics to these platforms, enabling consistent metric definitions and multidimensional analysis.

2. Performance Optimization. While cloud data warehouses have improved query performance through column-oriented storage and distributed processing, they still struggle with complex analytical workloads. Cube provides intelligent caching and pre-aggregation strategies that dramatically improve query response times.

3. Access Control and Governance. Securing and governing access to data across all consuming applications remains critical. Cube offers robust access control to ensure consistent security across your entire data ecosystem.

4. API Flexibility. Legacy OLAP tools were limited in how they exposed data. Cube provides modern REST, GraphQL, and SQL APIs along with support for traditional MDX and DAX interfaces, making it a truly universal semantic layer.

Cube is the missing OLAP engine for the cloud data platform era that provides the necessary infrastructure and features to implement efficient data modeling, access control, and performance optimizations without duplicating analytics modeling, data, or security permissions across different tools.

Cube Cloud is the fastest way to get started with Cube. It provides managed infrastructure as well as an instant and free access for development projects and proofs of concept.

For a step-by-step guide on Cube Cloud, see the docs.

Alternatively, you can get started with Cube locally or self-host it with Docker.

Once Docker is installed, in a new folder for your project, run the following command:

docker run -p 4000:4000 \
  -p 15432:15432 \
  -v ${PWD}:/cube/conf \
  -e CUBEJS_DEV_MODE=true \
  cubejs/cube

Then, open https://localhost:4000 in your browser to continue setup.

For a step-by-step guide on Docker, see the docs.

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There are many ways you can contribute to Cube! Here are a few possibilities:

• Star this repo and follow us on X.
• Add Cube to your stack on Stackshare.
• Upvote issues with 👍 reaction so we know what's the demand for particular issue to prioritize it within road map.
• Create issues every time you feel something is missing or goes wrong.
• Ask questions on Stack Overflow with cube.js tag if others can have these questions as well.
• Provide pull requests for all open issues and especially for those with help wanted and good first issue labels.

All sort of contributions are welcome and extremely helpful 🙌 Please refer to the contribution guide for more information.

Cube Client is MIT licensed.

Cube Backend is Apache 2.0 licensed.