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code of conduct contributing license security more items website docs examples blog slack x cube core is the open source semantic layer define metrics dimensions joins and access rules once in code then expose them through sql rest and graphql apis to anything downstream bi tools custom applications or ai agents cube core is headless it doesn t ship a ui so you can build the analytics experience that fits your product cube core works with all sql data sources including cloud data warehouses like snowflake databricks and bigquery query engines like presto and amazon athena and application databases like postgres it has a built in relational caching engine to provide sub second latency and high concurrency for api requests learn more about connecting cube to data sources and analytics visualization tools why cube core every bi tool relies on a semantic layer as its core engine the component that defines metrics dimensions and business logic and hides the complexity of the underlying data sources most semantic layers are proprietary tightly coupled to a single bi platform and can t be reused across other tools cube core is an open standalone semantic layer that any analytics application or ai agent can consume through standard apis define your metrics once and use them everywhere internal bi embedded analytics ai agents without re implementing the model in each place getting started you can run cube core locally or self host it with docker once docker is installed in a new folder for your project run docker run p 4000 4000 p 15432 15432 v pwd cube conf e cubejs_dev_mode true cubejs cube then open http localhost 4000 in your browser to continue setup for a step by step guide see the docs cube core vs cube cube is our commercial product an agentic analytics platform built on cube core same semantic layer underneath plus the rest of what makes it a full bi platform analytics chat workbooks and dashboards embedded analytics surfaces managed deployment rbac multi tenancy and integrations with tableau power bi excel and google sheets the data model is fully compatible both ways a model you build in cube core runs unchanged in cube and vice versa cube core stays open source and is what we run inside cube ourselves use cube core when you want to own the stack a custom bi experience deeply integrated embedded analytics or ai agents that need a governed semantic foundation use cube when you want a managed full featured bi platform out of the box internal analytics or customer facing embedded analytics without building the surrounding platform yourself for more on how we think about the split see the future of cube core and cube for a tour of what s in cube today watch the workshop or try cube for free resources documentation getting started examples tutorials architecture contributing there are many ways you can contribute to cube core 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 the demand is for particular issues to prioritize them within the roadmap create issues every time you feel something is missing or goes wrong ask questions on stack overflow with cube js tag if others might 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 sorts of contributions are welcome and extremely helpful please refer to the contribution guide for more information license cube client is mit licensed cube backend is apache 2 0 licensed about cube core is open source semantic layer for ai bi and embedded analytics cube dev topics mysql rust bigquery bi sql ai analytics businessintelligence postgresql snowflake business intelligence cube agents databricks semantic layer embedded analytics headless bi conversational analytics agentic analytics resources readme license view license code of conduct code of conduct contributing contributing security policy security policy uh 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