How do you manage schema evaluation when implementing CDC in a system that requires ongoing modifications in the source transactional database?

When implementing CDC in a system that requires ongoing modifications in the source transactional database, schema evaluation must be continuous. First, maintain a schema registry that records every change. Use automated drift detection to compare the current source schema with the last known good version. If a change is detected, update the CDC connector configuration (e.g., Debezium's 'schema.history.internal.kafka.topic') and regenerate the target table schema. Backward compatibility is critical: new columns can be added with default values, while removed columns should be archived or dropped with caution. Automated tests should run after each change to verify that the CDC stream still produces correct events. By integrating schema evaluation into the CI/CD pipeline, you can catch incompatible changes early and keep the CDC pipeline stable.