TechPrufer
Get started
Lyft
Lyft logoLyft · Feature trail

Metric Semantic Layer

1 post · Jun 2026

What this trail covers

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. The MSL is integrated with Amundsen for discoverability and a self-service UI for custom SQL generation. It also serves as a knowledge base for AI Agents and Skills, enabling natural language querying of metrics.

All Lyft trails
  1. Metric Semantic Layer: How Lyft Governs and Scales Key Data DefinitionsThis post details the implementation of Lyft's internal Metric Semantic Layer (MSL) as a Python package. It elaborates on the use of YAML configurations for metric definitions and Jinja SQL templates for dynamic SQL generation, emphasizing DRY principles. The post outlines the governance model, defining 'Golden Metrics' and establishing dual Business and Operational Owner roles for metric health and data health respectively. It also describes access methods including integration with Amundsen for discoverability, a self-service UI, and its application as a knowledge base for AI Agents and Skills, including the use of an MCP for natural language metric querying.