How should the Clarity data model be versioned and upgraded over time?

How should the Clarity data model be versioned and upgraded over time?

• A. Use version control for schema, apply backward-compatible changes, maintain migration scripts, and verify with tests and data reconciliation.
• B. Always implement non-backward-compatible changes directly.
• C. Versioning is optional.
• D. Migrate without tests.

Answer: (A)

Versioning a data model is about controlled, safe evolution over time. The recommended approach is to keep schema changes under version control so every modification is tracked, auditable, and reversible if needed. Apply changes in a backward-compatible way whenever possible so existing users, queries, and integrations keep functioning without immediate disruption. When a breaking change is unavoidable, provide migration scripts that transform existing data to the new schema and update dependent components accordingly. Pair this with tests that exercise the upgrade path and data reconciliation to verify that data integrity and expected behavior are preserved after migration. This combination reduces risk, supports reliable rollbacks, and ensures a smooth transition for downstream consumers. Choosing non-backward-compatible changes as the default, keeping versioning optional, or migrating without tests all introduce avoidable risk: breaking existing workflows, removing governance, and leaving data integrity unchecked.

Versioning a data model is about controlled, safe evolution over time. The recommended approach is to keep schema changes under version control so every modification is tracked, auditable, and reversible if needed. Apply changes in a backward-compatible way whenever possible so existing users, queries, and integrations keep functioning without immediate disruption. When a breaking change is unavoidable, provide migration scripts that transform existing data to the new schema and update dependent components accordingly. Pair this with tests that exercise the upgrade path and data reconciliation to verify that data integrity and expected behavior are preserved after migration. This combination reduces risk, supports reliable rollbacks, and ensures a smooth transition for downstream consumers.

Choosing non-backward-compatible changes as the default, keeping versioning optional, or migrating without tests all introduce avoidable risk: breaking existing workflows, removing governance, and leaving data integrity unchecked.