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Answer: Utilizing the SALTING technique by introducing a random prefix to the join keys.
The SALTING technique is a powerful method to address data skew by adding a random prefix to join keys, thereby distributing data more evenly across partitions. This approach mitigates the uneven workload distribution caused by skewed data, enhancing query performance. Data skew, where certain keys dominate the dataset, leads to inefficient processing. SALTING spreads out these keys, ensuring a balanced workload and preventing bottlenecks. This technique not only improves performance but also scales well with large datasets in Delta Lake, making it an ideal solution for handling skewed data efficiently.
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When facing performance issues in a Spark job due to skewed data in Delta Lake, which advanced technique can effectively reduce the impact of data skew on query performance?
A
Utilizing the SALTING technique by introducing a random prefix to the join keys.
B
Applying broadcast join as a solution for automatically managing skewed datasets.
C
Executing repartition(1) to merge all data into a single partition.
D
Using coalesce() without shuffling to decrease the number of partitions.
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