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Answer: Use Databricks Spark UI for streaming jobs to view real-time performance metrics
The Databricks Spark UI is the most suitable tool for real-time performance monitoring of streaming data jobs built with Databricks Structured Streaming. It offers a comprehensive dashboard that displays real-time metrics such as processing time, input rate, output rate, and error rate, essential for assessing the health and efficiency of your streaming data pipeline. This tool enables quick identification of bottlenecks or issues, allowing for immediate optimization. Additionally, it supports setting up alerts based on predefined thresholds for proactive monitoring. While alternatives like Azure Stream Analytics, Azure Event Hubs, and custom logging can be used, the Databricks Spark UI is specifically designed for Databricks platform jobs, providing the most detailed and immediate insights into your streaming data pipeline's performance.
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How can you monitor the performance of a Databricks Structured Streaming job in real-time for immediate insights and alerts?
A
Implement custom logging within the streaming jobs to send metrics to an external real-time monitoring solution
B
Configure Azure Event Hubs to capture streaming metrics and analyze them in Azure Monitor
C
Use Databricks Spark UI for streaming jobs to view real-time performance metrics
D
Integrate with Azure Stream Analytics for monitoring Databricks streaming jobs
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