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Answer: Data skew occurs when data is unevenly distributed across partitions, leading to inefficient resource utilization and slower processing times.
Data skew in PySpark occurs when data is unevenly distributed across partitions, causing some partitions to be much larger than others. This leads to inefficient resource utilization and slower processing times. Strategies to detect and mitigate data skew include using repartitioning based on key columns, applying salting techniques, and analyzing data distribution before processing.
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Explain the concept of data skew in PySpark and its impact on performance. Discuss strategies to detect and mitigate data skew in your data processing jobs.
A
Data skew occurs when data is unevenly distributed across partitions, leading to inefficient resource utilization and slower processing times.
B
Data skew is not a concern in PySpark as the framework automatically balances data across partitions.
C
Data skew only affects storage and has no impact on processing performance.
D
Data skew can be mitigated by always using the default number of partitions, which ensures even data distribution.
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