Semantic Layer

A practical example

Example: 'active customer' is defined once with its filters and time grain; the BI dashboard, the reverse-ETL sync and an AI analyst all resolve the same definition instead of restating SQL.

What to evaluate before investing

• Verify native integration with the tools your team actually uses; a layer that requires exporting CSVs or custom APIs will be bypassed.
• Check governance workflow: version control, review and certification of metric changes, plus clear deprecation paths.
• Test query performance and caching: the layer adds a hop, and slow metric queries push analysts back to hand-written SQL.

Limitations and tradeoffs

A semantic layer standardizes definitions only if it becomes the default query path; if analysts keep writing raw SQL against tables, two sources of truth coexist and drift returns.