Spark's repartition() is vital for managing data skewness, optimizing memory, and enhancing pipeline performance, especially post joins or aggregations.
Repartitioning can redistribute skewed data evenly across partitions, prevent out-of-memory errors, control file output, and improve query performance by organizing data efficiently.
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