Lyft developed a Metric Semantic Layer (MSL) as a centralized Python package to standardize metric definitions across the organization.
- •Implemented using YAML configurations and Jinja SQL templates to ensure DRY principles and flexible metric generation based on time granularity and dimensions
- •Established governance through Golden Metrics selection criteria (metrics with at least two distinct use cases) and mandatory dual ownership (Business Owner and Operational Owner)
- •Exposed metrics via Python APIs for integration with analytics dashboards, Airflow, ML models, and AI agents, with automated deployment updates
- •Enabled user access through Amundsen data catalog for discovery, self-service Metric UI for custom SQL generation, and AI MCP/Skills integration
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Lyft
Databricks