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Answer: Configuring Azure Application Insights to track and alert on latency metrics for data processing jobs in Databricks
In scenarios where data pipeline latency directly impacts user experience, real-time monitoring is essential for quick troubleshooting and optimization. Configuring Azure Application Insights for tracking and alerting on latency metrics in Databricks jobs is the optimal approach due to its real-time monitoring, alerting capabilities, seamless Databricks integration, and scalability. This ensures immediate identification and resolution of latency issues, maintaining optimal performance and user satisfaction.
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In a scenario where the latency of your data pipeline directly affects user experience, which method provides real-time monitoring of this latency to facilitate immediate troubleshooting and optimization?
A
Streaming job execution logs to Azure Event Hubs and using a real-time analytics service to monitor latency
B
Using the Databricks Spark UI‘s event timeline and job execution details to manually track job latency
C
Configuring Azure Application Insights to track and alert on latency metrics for data processing jobs in Databricks
D
Implementing custom metrics within your data processing jobs in Databricks to track and report latency to Azure Monitor
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