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When dealing with data skew in a Spark job that processes data from Azure Blob Storage, which of the following techniques is least effective for managing skew?
A
Applying a custom partitioner that considers data distribution
B
Employing salting techniques before shuffling
C
Using the coalesce function to reduce the number of partitions
D
Increasing the spark.sql.shuffle.partitions parameter
