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Answer: Analyze and refactor your Spark jobs to better handle data skew, such as through salting techniques, and fine-tune the Spark configurations for optimal resource utilization.
Analyzing and refactoring your Spark jobs to better handle data skew is essential for optimizing both cost and performance. Data skew leads to uneven workload distribution, which can be mitigated by techniques like salting. Fine-tuning Spark configurations ensures efficient resource use, reducing both time and cost. Simply increasing worker nodes or changing data formats doesn't address the root causes of inefficiency. Scheduling jobs during off-peak hours may reduce costs but doesn't improve job performance or resource utilization.
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Your Spark jobs on Databricks are running longer and becoming too costly, with suspicions of inefficient resource use and data skew. What optimization strategy would you implement?
A
Convert all your data to Delta format, assuming it will automatically optimize all operations.
B
Schedule jobs to run during off-peak hours to benefit from reduced cost, without changing job configurations.
C
Increase the number of worker nodes in your Databricks cluster to reduce job completion time.
D
Analyze and refactor your Spark jobs to better handle data skew, such as through salting techniques, and fine-tune the Spark configurations for optimal resource utilization.
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