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In the context of optimizing Spark applications on Azure Databricks, you are analyzing event timelines and metrics for stages and jobs performed on a cluster. Considering factors such as cost efficiency, compliance with data processing standards, and scalability, which of the following best describes the insights you can gain from this data and how it can be leveraged to enhance application performance? Choose the best option from the four provided.
A
The data is primarily useful for compliance auditing and does not offer actionable insights for performance optimization.
B
The data can highlight stages with prolonged execution times but fails to provide detailed insights into task-level execution or data skew issues.
C
The data offers comprehensive insights into task execution patterns, data distribution, and resource utilization, enabling targeted optimizations such as adjusting parallelism, repartitioning data, or refining transformations to improve efficiency and reduce costs.
D
The data's utility is limited to identifying task failures, with no relevance to performance tuning or scalability improvements.