Data integrity checks in Testing Fundamentals - Framework Patterns

Framework Mode - Data integrity checks

Folder Structure for Data Integrity Checks Framework

  data-integrity-checks-project/
  ├── config/
  │   ├── environments.yaml       # Environment-specific settings
  │   └── credentials.yaml        # Secure storage for DB/API credentials
  ├── data/                      # Sample data files for testing
  │   ├── input/
  │   └── expected/
  ├── src/
  │   ├── checks/                # Data integrity check implementations
  │   │   ├── uniqueness_check.py
  │   │   ├── referential_integrity_check.py
  │   │   └── schema_validation_check.py
  │   ├── utils/                 # Helper functions (DB connectors, data loaders)
  │   │   └── db_connector.py
  │   └── tests/                 # Test cases for data integrity
  │       ├── test_uniqueness.py
  │       ├── test_referential_integrity.py
  │       └── test_schema_validation.py
  ├── reports/                   # Test execution reports
  ├── requirements.txt           # Dependencies
  └── pytest.ini                 # Pytest configuration
  

Test Framework Layers for Data Integrity Checks

• Configuration Layer: Holds environment details, credentials, and test data paths.
• Utility Layer: Contains reusable helpers like database connectors and data loaders.
• Check Implementation Layer: Contains specific data integrity check functions (e.g., uniqueness, referential integrity, schema validation).
• Test Layer: Contains test scripts that call check functions and assert results.
• Reporting Layer: Collects and formats test results for review.

Configuration Patterns

• Environment Files: Use YAML or JSON files to store environment-specific info like database URLs and credentials.
• Secure Credentials: Store sensitive info separately and load securely during test runtime.
• Parameterization: Use test framework features (e.g., pytest fixtures) to inject config data into tests.
• Data Files: Keep input and expected data in separate folders for clarity and easy updates.

Test Reporting and CI/CD Integration

• Use pytest built-in reporting or plugins like pytest-html to generate readable HTML reports.
• Save reports in a dedicated reports/ folder for easy access.
• Integrate tests into CI/CD pipelines (e.g., GitHub Actions, Jenkins) to run data integrity checks automatically on code changes.
• Fail the pipeline if any data integrity check fails to prevent bad data from progressing.

Best Practices for Data Integrity Check Frameworks

• Modular Checks: Write each data integrity check as a separate, reusable function or class.
• Clear Assertions: Make assertions descriptive to quickly identify which check failed and why.
• Use Realistic Test Data: Use sample data that mimics production data to catch real issues.
• Isolate Tests: Ensure tests do not depend on each other to avoid cascading failures.
• Automate and Schedule: Run data integrity tests regularly to catch issues early.

Self Check Question

Where in this folder structure would you add a new data integrity check for verifying data type consistency?

Key Result

Organize data integrity checks into modular layers with clear config, reusable utilities, and automated reporting.