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Answer: Configure Azure Load Testing service to mimic real-time data ingestion patterns, analyzing performance in Databricks.
The correct approach is to configure the Azure Load Testing service to mimic real-time data ingestion patterns and analyze performance in Databricks. This method ensures realistic data streams for testing, accurate measurement of throughput and latency, and seamless integration with Databricks for performance analysis. It offers scalability and efficiency, providing a comprehensive view of the pipeline's performance under load.
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How would you conduct load testing for a streaming data pipeline built with Azure Databricks processing data from Event Hubs to ensure it can handle peak data volumes?
A
Deploy a custom data generator in AKS to produce data streams, utilizing Azure Monitor for performance metrics.
B
Manually insert data into Event Hubs and observe the processing in Databricks, adjusting volumes to simulate peak loads.
C
Configure Azure Load Testing service to mimic real-time data ingestion patterns, analyzing performance in Databricks.
D
Use Apache JMeter to simulate Event Hubs data streams and monitor pipeline metrics in Databricks.
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