For geo-distributed teams using Azure Databricks, ensuring data consistency and synchronization involves both orchestration and validation of data pipelines. Azure Data Factory (ADF) excels at:

• Orchestrating complex ETL workflows across multiple locations and systems,
• Monitoring data pipelines in real time with alerting and logging,
• Running consistency checks using Databricks notebooks or jobs as part of the pipeline steps.

This approach is scalable, automated, and integrates well with other Azure services.

Why the other options are less correct:

• A: Periodic validation jobs and logging are useful, but lack orchestration of actual data movement and don’t ensure synchronization by themselves.
• B: Delta Lake and Unity Catalog provide data reliability and governance, but not orchestration or cross-region consistency checks. They’re part of the solution but not the primary mechanism for monitoring and synchronization.
• D: Manual coordination and emails are prone to human error and not a scalable or reliable strategy.