reading-data-dict

To find official metric definitions before writing analytics SQL, look up dbt manifests, data dictionaries, metric YAML files, and model docs. This surfaces authoritative definitions, lineage, grain, and filters to ensure your queries match stakeholder expectations and product standards.

Analytics SQL returns incorrect metrics when business and product terms are unmapped to underlying data models and official definitions. Cross-referencing dbt project documentation, data dictionaries, and semantic layer configurations before query development prevents inconsistent metric calculations.

Validate column definitions by cross-referencing documented semantics against observed live schemas. This discrepancy flagging process reads dbt manifests, metric YAML files, and model docs to surface conflicts between documented semantics and actual schema before you write SQL.

Yes, mapping business terms to curated data models applies directly to semantic layer configurations. It locates and reads project data sources including metric YAML files and dbt manifests to retrieve authoritative lineage, grain, filters, and caveats for analytics engineering and BI workflows.

The best way to prevent inconsistent metric calculations in BI workflows is to surface authoritative metric definitions from project documentation sources before query development. Reading data dictionaries and dbt docs ensures your retention analysis uses the correct join keys and official definitions.