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Answer: Use Spark's adaptive query execution to dynamically adjust the data partitioning.
Spark's adaptive query execution allows for dynamic adjustments to the data partitioning during runtime, which can effectively handle data skew by rebalancing the data across nodes. This approach optimizes resource utilization and improves the overall performance of the Spark job by addressing the skew issue proactively.
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In a distributed data processing environment, data skew can significantly impact performance. Describe a scenario where you encounter data skew in a Spark job and outline the steps you would take to mitigate this issue. Include specific techniques or configurations you would apply to balance the data distribution across nodes.
A
Increase the number of partitions without changing the data distribution strategy.
B
Use Spark's adaptive query execution to dynamically adjust the data partitioning.
C
Ignore the skew as it is a natural part of data processing.
D
Manually redistribute the data before loading it into Spark.
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