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Answer: Utilize Apache JMeter to generate data ingestion requests to the pipeline and monitor performance metrics in Azure Monitor.
1. Apache JMeter is a widely used open-source tool for performance testing that allows you to simulate real-world data loads by generating requests to the data ingestion pipeline. 2. By using Apache JMeter, you can create different scenarios with varying loads to test the performance of the data ingestion pipeline under different conditions. 3. Azure Monitor is a comprehensive monitoring and analytics service that can be used to track the performance metrics of the data ingestion pipeline in real-time. 4. By combining Apache JMeter with Azure Monitor, you can accurately measure the ingestion rates and latency of the pipeline and identify any bottlenecks or performance issues before deploying it into production. 5. This approach provides a reliable and efficient way to benchmark the performance of the data ingestion pipeline and ensure that it meets the required performance standards for production deployment.
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Before deploying a new data ingestion pipeline into production, you need to benchmark its performance. Which tool or approach would you use to simulate real-world data loads and measure ingestion rates and latency?
A
Create a custom simulation in Databricks notebooks that generates data at varying loads and measures ingestion times directly.
B
Manually inject data using Azure Data Factory and monitor the ingestion performance through Databricks Spark UI.
C
Utilize Apache JMeter to generate data ingestion requests to the pipeline and monitor performance metrics in Azure Monitor.
D
Employ Azure Load Testing service to simulate high-volume data ingestion and capture performance metrics using Databricks‘ built-in monitoring.