Designing a Metrics Layer for Fast Decisions
Data engineering patterns for metrics layers that keep analytics consistent at scale.
The metrics explosion problem
As teams grow, everyone builds their own dashboards. Soon you have 10 different definitions of “revenue” and no one knows which is correct.
Centralize definitions, not data
A metrics layer should:
- Define metrics once in a single source of truth
- Compute metrics consistently across all consumers
- Version metric definitions to track changes over time
- Document business logic so stakeholders understand calculations
The architecture
- Metrics registry: Central catalog of metric definitions
- Metrics store: Pre-computed metrics for fast queries
- Metrics API: Consistent interface for all consumers
- Metrics lineage: Track dependencies and transformations
Implementation patterns
dbt metrics: Use dbt for SQL-based metric definitions Looker: Use LookML for semantic layer definitions Custom layer: Build your own if you need specific governance
The migration strategy
Start by documenting existing metrics. Then gradually migrate dashboards to use centralized definitions. Don’t try to migrate everything at once.