Explanation:
Option B is the correct approach as it leverages Azure Databricks' built-in logging and monitoring features for tracking data processing activities. The Databricks Unified Analytics Platform provides comprehensive monitoring and logging capabilities, allowing for effective auditing and troubleshooting. Options A, C, and D do not provide the same level of control and flexibility for monitoring and logging data processing activities in the context of batch processing.
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In your batch processing solution, you need to implement a mechanism to monitor and log the data processing activities for auditing and troubleshooting purposes. How would you implement this functionality?
A
Use Azure Data Factory's monitoring and logging features to track the data processing activities.
B
Use Azure Databricks to process the data and leverage its built-in logging and monitoring features, such as the Databricks Unified Analytics Platform.
C
Use Azure Stream Analytics to process the data in real-time and leverage its built-in monitoring and logging features.
D
Use Azure Functions to process the data in small batches and implement custom logging and monitoring solutions using Application Insights.
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