---
id: SKL-data-DATACONTRACTS
name: Data Contracts
description: A Data Contract is a formal agreement between a data producer (e.g.,
  a microservice) and a data consumer (e.g., a data platform) that defines the structure,
  semantics, and quality of data being shared
version: 1.0.0
status: active
owner: '@cerebra-team'
last_updated: '2026-02-22'
category: Backend
tags:
- api
- backend
- server
- database
stack:
- Python
- Node.js
- REST API
- GraphQL
difficulty: Intermediate
---

# Data Contracts

## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**

## Overview
A Data Contract is a formal agreement between a data producer (e.g., a microservice) and a data consumer (e.g., a data platform) that defines the structure, semantics, and quality of data being shared. It acts as a "service-level agreement" (SLA) for data.

**Core Principle**: "Data should be treated as a product, and products need specifications."

## Why This Matters
- **Stability**: Contract ensures stable data interfaces
- **Quality**: Schema validation prevents data quality issues
- **Accountability**: Contract violations are tracked and addressed
- **Efficiency**: Clear specifications reduce development time
- **Security**: Contract terms protect sensitive data

---

## Core Concepts & Rules

### 1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs

### 2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment


## Inputs / Outputs / Contracts
* **Inputs**:
  - Schema definitions
  - Data events and messages
  - Quality requirements
  - SLA definitions
* **Entry Conditions**:
  - Schema is defined
  - Validation rules are established
  - Monitoring is configured
* **Outputs**:
  - Validated events
  - Contract violations
  - Quality reports
  - SLA compliance data
* **Artifacts Required (Deliverables)**:
  - Schema files (YAML/JSON)
  - Validation logic
  - Monitoring dashboards
  - Contract documentation
* **Acceptance Evidence**:
  - All events are validated
  - Violations are tracked
  - SLAs are met
  - Quality is maintained
* **Success Criteria**:
  - Validation accuracy > 99%
  - SLA compliance > 99.5%
  - Contract violations < 0.1%

## Skill Composition
* **Depends on**: Schema Management, Data Quality Monitoring
* **Compatible with**: Data Quality Rules, Data Validation Rules
* **Conflicts with**: Systems without schema validation
* **Related Skills**: 
  - [43-data-reliability/schema-management](43-data-reliability/schema-management/SKILL.md) - Schema versioning
  - [43-data-reliability/data-quality-monitoring](43-data-reliability/data-quality-monitoring/SKILL.md) - Quality monitoring
  - [41-incident-management/incident-triage](41-incident-management/incident-triage/SKILL.md) - Incident handling

---

## Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

```python
# Example implementation following best practices
def example_function():
    # Your implementation here
    pass
```


## Assumptions / Constraints / Non-goals

* **Assumptions**:
  - Development environment is properly configured
  - Required dependencies are available
  - Team has basic understanding of domain
* **Constraints**:
  - Must follow existing codebase conventions
  - Time and resource limitations
  - Compatibility requirements
* **Non-goals**:
  - This skill does not cover edge cases outside scope
  - Not a replacement for formal training


## Compatibility & Prerequisites

* **Supported Versions**:
  - Python 3.8+
  - Node.js 16+
  - Modern browsers (Chrome, Firefox, Safari, Edge)
* **Required AI Tools**:
  - Code editor (VS Code recommended)
  - Testing framework appropriate for language
  - Version control (Git)
* **Dependencies**:
  - Language-specific package manager
  - Build tools
  - Testing libraries
* **Environment Setup**:
  - `.env.example` keys: `API_KEY`, `DATABASE_URL` (no values)


## Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks |
| :--- | :--- | :--- |
| **Unit** | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| **Integration** | DB / API | All external API calls or database connections must be mocked during unit tests |
| **E2E** | User Journey | Critical user flows to test |
| **Performance** | Latency / Load | Benchmark requirements |
| **Security** | Vuln / Auth | SAST/DAST or dependency audit |
| **Frontend** | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |


## Technical Guardrails & Security Threat Model

### 1. Security & Privacy (Threat Model)
* **Top Threats**: Injection attacks, authentication bypass, data exposure
- [ ] **Data Handling**: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- [ ] **Secrets Management**: No hardcoded API keys. Use Env Vars/Secrets Manager
- [ ] **Authorization**: Validate user permissions before state changes

### 2. Performance & Resources
- [ ] **Execution Efficiency**: Consider time complexity for algorithms
- [ ] **Memory Management**: Use streams/pagination for large data
- [ ] **Resource Cleanup**: Close DB connections/file handlers in finally blocks

### 3. Architecture & Scalability
- [ ] **Design Pattern**: Follow SOLID principles, use Dependency Injection
- [ ] **Modularity**: Decouple logic from UI/Frameworks

### 4. Observability & Reliability
- [ ] **Logging Standards**: Structured JSON, include trace IDs `request_id`
- [ ] **Metrics**: Track `error_rate`, `latency`, `queue_depth`
- [ ] **Error Handling**: Standardized error codes, no bare except
- [ ] **Observability Artifacts**:
    - **Log Fields**: timestamp, level, message, request_id
    - **Metrics**: request_count, error_count, response_time
    - **Dashboards/Alerts**: High Error Rate > 5%


## Agent Directives & Error Recovery
*(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)*

- **Thinking Process**: Analyze root cause before fixing. Do not brute-force.
- **Fallback Strategy**: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- **Self-Review**: Check against Guardrails & Anti-patterns before finalizing.
- **Output Constraints**: Output ONLY the modified code block. Do not explain unless asked.


## Definition of Done (DoD) Checklist

- [ ] Tests passed + coverage met
- [ ] Lint/Typecheck passed
- [ ] Logging/Metrics/Trace implemented
- [ ] Security checks passed
- [ ] Documentation/Changelog updated
- [ ] Accessibility/Performance requirements met (if frontend)


## Anti-patterns / Pitfalls

* ⛔ **Don't**: Log PII, catch-all exception, N+1 queries
* ⚠️ **Watch out for**: Common symptoms and quick fixes
* 💡 **Instead**: Use proper error handling, pagination, and logging


## Reference Links & Examples

* Internal documentation and examples
* Official documentation and best practices
* Community resources and discussions


## Versioning & Changelog

* **Version**: 1.0.0
* **Changelog**:
  - 2026-02-22: Initial version with complete template structure