Continuous Delivery for Data • Dave Farley • YOW! 2020

Continuous Delivery should be applied to data changes as well as code changes - the ability to deploy schema changes safely is crucial for true CD

Three main categories of data to consider:

• Transactional data (generated during system operation)
• Reference/lookup data (static, read-only)
• Configuration data (defines system behavior)

Key data migration patterns:

• Deployment time migration (simple but requires downtime)
• Lazy reader (translates on read, good for hot deployments)
• Lazy migrator (background migration during idle time)

Version control everything:

• Schema changes
• Migration scripts
• Data models
• Configuration
• Infrastructure code

Best practices for schema changes:

• Make additive changes when possible
• Version schemas with sequential numbers
• Keep schema version info with application code
• Write and test both upgrade and rollback scripts
• Test migrations thoroughly

Data testing approaches:

• Generate synthetic test data in test scope
• Avoid using production data for tests
• Focus on testing migration logic, not just final state
• Include migration tests in CI pipeline

For machine learning systems:

• Version control training data and models
• Create deployment pipelines for ML models
• Monitor model performance in production
• Enable A/B testing of models
• Plan for model updates and retraining

Make systems deterministic and repeatable:

• Use infrastructure as code
• Automate environment setup
• Version all dependencies together
• Enable rolling back to previous states

Design systems to handle evolution:

• Allow structure to change over time
• Plan for data migration needs upfront
• Keep old data versions readable
• Build migration capabilities into applications

Focus on fast feedback loops:

• Automate testing and deployment
• Make changes in small increments
• Validate changes early
• Monitor results in production