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How can you dynamically scale Spark resources in a Spark Streaming application consuming data from Kafka, especially when the workload varies significantly throughout the day?
A
Manually scale the Spark cluster based on expected workload patterns.
B
Implement a feedback loop using Spark‘s StreamingListener to adjust streaming rates and executor allocation.
C
Utilize Kafka‘s consumer group metrics to trigger scaling actions in the Spark streaming application.
D
Configure Spark‘s dynamic allocation feature to automatically scale based on the rate of incoming Kafka messages.
