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How can you deploy updates to critical data pipelines in Azure Databricks without causing downtime or data loss?
A
Using rolling updates across Databricks clusters, updating notebook versions incrementally and monitoring for errors
B
Creating shadow pipelines in Databricks that process real-time data in parallel with the production pipeline, comparing outputs before cutover
C
Implementing blue/green deployments by setting up parallel pipelines, gradually shifting traffic to the new version after thorough testing
D
Deploying new versions during off-peak hours, utilizing Azure Databricks jobs for immediate rollback in case of failures
