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Answer: Implementing an automated version control system with Databricks Repos, enabling quick rollbacks to previous notebook versions based on deployment markers
Implementing an automated version control system with Databricks Repos is the most suitable and efficient strategy for ensuring minimal downtime and data integrity in the event of a failed deployment. This approach allows for: 1. **Automated Version Control**: Tracking changes to notebooks and artifacts, enabling easy reversion to previous versions. 2. **Deployment Markers**: Tagging specific notebook versions for quick identification and rollback to the last successful deployment. 3. **Granular Control**: Offering detailed version history for comparison, merging, and selective rollback, crucial for complex pipelines. 4. **Collaboration and Traceability**: Centralizing notebook management and enhancing traceability through version history and deployment markers. This strategy not only addresses deployment failures effectively but also improves collaboration and control over the deployment process in Azure Databricks.
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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
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