This post covers semantic layer architecture — the component that translates source data into governed business metrics for consistent access across BI tools, notebooks, and AI systems.
- •Core components include dimensions (categorical axes), measures (ARR, NRR, churn rate), joins, filters with embedded business rules, and a metadata/governance layer.
- •Platform-native semantics define logic once in the data platform and expose it via APIs, replacing BI-tool-bound approaches like DAX and LookML that cause definition drift across teams.
- •The semantic layer evolved from 1990s OLAP cubes through LookML to modern headless layers such as Cube, AtScale, and dbt Semantic Layer.
- •Shared caching and materialization serve both human analysts and LLM-powered interfaces from the same pre-computed results.
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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