[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-semantic-layer::en":3,"gloss-cluster-semantic-layer::en":23,"gloss-next-semantic-layer::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"semantic-layer","analytics","Semantic Layer","A semantic layer is a centralized definition of your business metrics — what `active user`, `MRR`, or `gross margin` actually mean in SQL — sitting between your data warehouse and every tool that queries it. Instead of each dashboard, notebook, and BI tool re-implementing the revenue calculation (and getting slightly different numbers), they all call the same governed definition. dbt Semantic Layer, Cube, and Looker's LookML are the common implementations. For builders, the pain it solves is the meeting where finance, product, and the CEO each quote a different `MRR` and nobody knows which is right; the semantic layer makes the metric a single versioned artifact. It also decouples metric logic from the presentation tool, so switching BI vendors doesn't mean rewriting every formula. Practical note: it's most valuable once you have multiple consumers of the same data; a solo founder with one dashboard doesn't need one yet. Define metrics as code, review changes in pull requests, and keep dimensions consistent.","A semantic layer centralises what your metrics actually mean in SQL, so every dashboard and BI tool computes MRR the same way instead of each inventing its own.",null,[11,14,17,20],{"slug":12,"name":13},"data-warehouse","Data Warehouse",{"slug":15,"name":16},"materialized-view","Materialized View",{"slug":18,"name":19},"olap","OLAP (Online Analytical Processing)",{"slug":21,"name":22},"warehouse-native-analytics","Warehouse-Native Analytics",[24,28,31,35,38,41,44,47,50,53,56,59],{"slug":25,"category":5,"name":26,"updated_at":27},"ab-testing","A\u002FB Testing","2026-08-24T02:46:38+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"active-user","Active User (DAU, WAU, MAU)",{"slug":32,"category":5,"name":33,"updated_at":34},"autocapture","Autocapture","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":27},"cost-per-resolution","Cost per Resolution",{"slug":39,"category":5,"name":40,"updated_at":34},"customer-data-platform","Customer Data Platform (CDP)",{"slug":42,"category":5,"name":43,"updated_at":27},"deflection-rate","Deflection Rate",{"slug":45,"category":5,"name":46,"updated_at":27},"guardrail-metric","Guardrail Metric",{"slug":48,"category":5,"name":49,"updated_at":34},"identity-resolution","Identity Resolution",{"slug":51,"category":5,"name":52,"updated_at":34},"multi-touch-attribution","Multi-Touch Attribution",{"slug":54,"category":5,"name":55,"updated_at":27},"novelty-effect","Novelty Effect",{"slug":57,"category":5,"name":58,"updated_at":34},"retention-curve","Retention Curve",{"slug":60,"category":5,"name":61,"updated_at":27},"sample-ratio-mismatch","Sample Ratio Mismatch (SRM)"]