Semantic Layer
Metric definitions held once and served to every tool that asks.
5 to work through
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intermediate
A company adopts a central semantic layer so that "active customer" has one definition everywhere. Two years later analysts route around it. What did the organisation buy and what did it pay?
2 min answer -
advanced
A recommendation team maintains separate feature computation for offline training and online serving, and skew between them is degrading model quality. How does a feature store with point-in-time correctness fix this, and what does it cost operationally?
3 min answer -
advanced
Three teams report different numbers for the same metric and each is confident. What is the architectural cause, and what fixes it?
2 min answer -
advanced Multiple choice
Where should shared metric definitions live so that a dashboard, a notebook and an API all produce the same number?
1 min answer -
advanced
You build a semantic layer with governed metric definitions. Six months later teams are still writing their own SQL. Why, and what do you do?
2 min answer
4 terms in this topic
Airbnb Minerva: One Definition of a Metric
Airbnb built a central metrics platform because the same business question was producing different answers depending on which dashboard you opened.
patternMetric Definition Layer
Business metrics defined once in a versioned, tested place and served to every consuming tool, so the number cannot differ by which tool asked.
conceptPoint-in-Time Correctness
Retrieving each feature's value as it was at the moment of the event rather than its current value, which is what prevents a model from training on i…
patternSemantic Layer
A single governed definition of business metrics and entities, sitting between physical tables and every consuming tool.
Neighbouring topics
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
Data Mesh
Domain ownership, data as a product, self-serve platform, and federated governance.
Data Products
A dataset with an owner, an interface, an SLO, and consumers who can rely on it.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Data Catalog
Discovery, ownership and technical metadata, and why catalogues go stale.
Business Glossary
Agreeing what a term means before arguing about which number is right.
Master Data Management
One authoritative record for a customer or product across systems that each have their own.
Reference Data
Code lists, hierarchies and currencies — small, shared, and quietly load-bearing.
Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
Data Observability
Freshness, volume, schema and distribution monitoring for pipelines that fail silently.
Data Stewardship
The operating model that makes ownership a role rather than a slide.
Data Access Models
Role, attribute and purpose-based access over analytical data, and how they compose.
Row & Column-Level Security
Restricting slices of a table rather than the whole table, and where it is enforced.
Tokenisation & Masking
Dynamic masking, deterministic tokens, and preserving joinability without exposure.
Retention & Purge
Deleting from an append-only estate, and proving the deletion happened.
Data Sharing & Clean Rooms
Collaborating on data neither party may hand over, with computation as the interface.
Sensitivity Labelling
Propagating a classification through joins and derived tables so controls follow the data.
BI Governance
Dashboard sprawl, certified reports, and the number the board is allowed to see.
Self-Service vs Governed
Letting analysts move fast without four teams reporting four different revenues.