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How can you effectively benchmark Spark job performance in Azure Databricks against various cluster configurations and data volumes?
A
Utilize third-party benchmarking tools that integrate with Azure Databricks for automated performance testing across configurations.
B
Develop a custom Spark job benchmarking application within Databricks notebooks that dynamically adjusts to cluster configurations and data sizes.
C
Leverage Azure Monitor to track performance metrics across different runs, manually adjusting variables.
D
Use Databricks‘ built-in experiment tracking to log performance metrics under varied conditions, analyzing results with MLflow.
