Why Semantic Layers Matter (and how to build one with DuckDB)
5 days ago
- #semantic-layer
- #data-analytics
- #duckdb
- Semantic layers provide a unified place to define business metrics once, ensuring consistency across BI tools, web apps, and notebooks.
- They eliminate the need to re-implement metrics in multiple tools, improving maintainability and data governance.
- Semantic layers enable dynamic query rewriting, allowing users to query using business concepts without knowing underlying SQL complexity.
- They support caching for ad hoc queries, reducing data movement costs and improving query performance.
- Semantic layers provide unified access-level security across various APIs (REST, GraphQL, SQL, etc.), ensuring consistent permissions.
- They enhance LLM accuracy by providing business context, preventing hallucinations in natural language queries.
- A practical example demonstrates building a semantic layer using DuckDB, Ibis, and a YAML file for metric definitions.
- Semantic layers are not needed for simple analytics with one consumer or when all metrics are preprocessed into physical tables.
- Popular semantic layer tools include Cube, AtScale, dbt Semantic Layer, and GoodData.
- The article concludes by encouraging starting with simple implementations like the Boring Semantic Layer and scaling as needed.