Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
5 to work through
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intermediate
GitLab publishes its data quality approach in its open handbook: a Trusted Data Framework of SQL and YAML test cases with PASS or FAIL results, four test types, and tiered data assets. What problem does that shape solve that a schema registry does not, and what does it cost?
2 min answer -
advanced
A data platform introduces data contracts and pipelines still break when producers change schemas. What was missing?
2 min answer -
advanced
A producer team renames a column. The data contract is defined and the contract test is green. Three hours later four downstream models fail and a board dashboard is blank. The test lived in the consumer's repository and ran on the consumer's schedule. What is the structural defect?
3 min answer -
advanced
A schema change in one team's service breaks four downstream data consumers overnight. The contract existed. What failed?
1 min answer -
advanced
You propose data contracts. An application team says "we are not a data team, our database is our own business". How do you respond?
2 min answer
5 terms in this topic
Contract Enforcement Point
Where a data contract is actually checked - which determines whether a breaking change fails in the producer's build or in a consumer's pipeline at 2 a.m.
patternData Contract
An explicit agreement between a data producer and its consumers covering schema, semantics, quality and change policy - which converts an implicit de…
patternData Contract
An explicit, versioned, enforceable agreement between a data producer and its consumers covering schema, semantics, quality and delivery.
conceptProducer Obligation
What a data contract binds the producing team to do, and — crucially — what happens in their pipeline when they are about to break it.
practiceProducer-Enforced Contract
A data contract checked in the producing team's own pipeline, so a breaking change is blocked before it ships - without which the contract is documen…
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 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.
Semantic Layer
Metric definitions held once and served to every tool that asks.
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.