Which practice is most associated with managing data schema versioning in a CDX platform?

Which practice is most associated with managing data schema versioning in a CDX platform?

• A. Use semantic/versioned schemas
• B. Emit deprecation notices
• C. Provide migration plans
• D. Maintain backward compatibility

Answer: (C)

When a CDX platform evolves its data schema, the key need is a clear, actionable path from the current version to the next. Providing migration plans does exactly that. It documents what changes exist between versions, how existing data should be transformed to fit the new schema, and how to coordinate updates across producers and consumers. It also lays out rollout timing, compatibility considerations, and rollback options so deployments don’t disrupt downstream systems. Other practices—like using clearly versioned schemas, emitting deprecation notices, or aiming for backward compatibility—support the process, but they don’t by themselves specify the concrete steps and sequence required to move data and integrations from one schema version to another. A migration plan ties the versioning to practical changes, guiding data transformation, testing, and rollout in a coordinated way.

When a CDX platform evolves its data schema, the key need is a clear, actionable path from the current version to the next. Providing migration plans does exactly that. It documents what changes exist between versions, how existing data should be transformed to fit the new schema, and how to coordinate updates across producers and consumers. It also lays out rollout timing, compatibility considerations, and rollback options so deployments don’t disrupt downstream systems. Other practices—like using clearly versioned schemas, emitting deprecation notices, or aiming for backward compatibility—support the process, but they don’t by themselves specify the concrete steps and sequence required to move data and integrations from one schema version to another. A migration plan ties the versioning to practical changes, guiding data transformation, testing, and rollout in a coordinated way.