For large, complex systems, test data generation follows a structured process to ensure realism, security, and scalability.
- Discover & Classify: Identify key data entities and tag sensitive info.
- Model & Subset: Create smaller, representative datasets while maintaining relationships.
- Mask & Anonymize: Protect sensitive data using masking or tokenization.
- Generate Synthetic Data: Use AI tools (e.g., GenRocket, Tonic.ai) to fill gaps and create edge cases.
- Version & Automate: Manage datasets with Git/data lakes and provision via CI/CD.
- Validate & Refresh: Continuously check data integrity and sync with production updates.
Tools: Delphix, Informatica TDM, Snowflake, Airflow, GenRocket.
Blend real, masked, and synthetic data in an automated, versioned pipeline to keep tests consistent, secure, and production-relevant.