Data contracts are formal agreements between data producers (teams generating data) and data consumers (teams using data). They define what data will look like, its quality guarantees, and how changes will be communicated.
What Data Contracts Include
1. Schema Definition: Column names, types, constraints
2. Semantic Meaning: Business definitions and context
3. Quality SLAs: Freshness, completeness, accuracy guarantees
4. Ownership: Who is responsible for the data
5. Change Management: How breaking changes are handled
Why Data Contracts Matter
Traditional data pipelines are fragile:
- Upstream changes break downstream systems
- No clear ownership or accountability
- Quality issues discovered too late
- Implicit expectations cause confusion
Data contracts solve this by making expectations explicit.
Data Contract Example
``yaml``
name: orders
version: 1.0.0
owner: commerce-team
schema:
- name: order_id
type: string
required: true
- name: total_amount
type: decimal
required: true
quality:
freshness: 1 hour
completeness: 99%
Implementing Data Contracts
1. Define Standards: Create a contract template
2. Identify Critical Data: Start with key datasets
3. Negotiate Terms: Producers and consumers agree
4. Validate Automatically: Check contracts in CI/CD
5. Monitor Compliance: Track SLA adherence
Data Contract Tools
- Soda: Data quality with contracts
- Great Expectations: Contract-like expectations
- dbt Contracts: Schema contracts in dbt
- Datacontract.com: Open standard and CLI
- Monte Carlo: SLA monitoring