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How would you design a testing strategy to ensure your Spark streaming jobs on Azure Databricks dynamically scale resources based on incoming data volume to maintain processing SLAs?
A
Utilizing static datasets of different sizes to mimic streaming input, observing the cluster‘s response without actual streaming data.
B
Implementing a mock streaming service within Databricks that artificially creates load and tests the auto-scaling feature‘s responsiveness.
C
Simulating real-world streaming data using Azure Event Hubs to generate varying data volumes and monitoring Databricks‘ automatic scaling response using Azure Monitor metrics.
D
Manually adjusting the number of nodes in the Databricks cluster before each test run to observe changes in job completion times.
