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In the context of Azure Databricks, what advanced rollback strategy is most effective for minimizing downtime and ensuring data integrity during a failed deployment in a complex data transformation pipeline?
A
Maintaining shadow environments in parallel, switching traffic to the backup environment in case of deployment issues, and synchronizing state post-rollback
B
Creating immutable snapshots of the entire Databricks workspace, allowing for instant reversion to a pre-deployment state in case of critical failures
C
Implementing an automated version control system with Databricks Repos, enabling quick rollbacks to previous notebook versions based on deployment markers
D
Utilizing Azure DevOps release gates to automatically rollback deployments based on performance metrics or error rates exceeding thresholds
