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Metric Semantic Layer

Metric Semantic Layer

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posts
2026

Lyft built and launched an internal Metric Semantic Layer (MSL) as a Python package to standardize metric definitions. This layer provides a single source of truth for metric definitions, including plain-English descriptions and definitive SQL code, ensuring consistency across teams. It simplifies onboarding and change management through flexible YAML configurations and Jinja SQL templates, and enforces intentional governance with clear selection criteria for 'Golden Metrics' and a dual ownership model (Business and Operational Owners). Transparency is achieved through Python-based methods, APIs, integration with Amundsen for discoverability, a self-service Metric UI, and an MCP for AI agent integration.

2026

Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions

6/10/2026

Lyft implemented a Metric Semantic Layer (MSL) as a Python package. This involved defining metric structures using YAML configurations and Jinja SQL templates for flexibility and DRY principles. The system establishes selection criteria for 'Golden Metrics' and an ownership model with Business and Operational Owners for governance. Access methods include Python APIs, Amundsen integration, a self-service UI, and an MCP for AI agents.