How can you deploy updates to critical data pipelines in Azure Databricks without causing downtime or data loss?

Real Exam

Using rolling updates across Databricks clusters, updating notebook versions incrementally and monitoring for errors

10.3%

Creating shadow pipelines in Databricks that process real-time data in parallel with the production pipeline, comparing outputs before cutover

19.2%

Implementing blue/green deployments by setting up parallel pipelines, gradually shifting traffic to the new version after thorough testing

53.8%

Deploying new versions during off-peak hours, utilizing Azure Databricks jobs for immediate rollback in case of failures

16.7%

Databricks Certified Data Engineer - Professional