Summary

The article discusses Lyft's implementation of a Metric Semantic Layer (MSL) to standardize data definitions across teams. As Lyft scaled, different teams used varying definitions for metrics, leading to inconsistencies in decision-making. The MSL serves as a centralized repository for metric definitions, ensuring clarity, governance, and accessibility. The article outlines the principles behind MSL, including simplified onboarding, intentional governance, and transparency, along with technical details about its implementation using Python and YAML configurations.

💭 Your Thoughts

Pbm: we found ourselves at risk of different teams using different definitions for a given metric. centralized repository that serves as a single, authoritative home for every metric’s definition “Golden Metrics”. These are metrics that have at least two distinct use cases or applications.

Data Problems Addressed