What is repartitioning in Spark, and when would you use it?

Repartitioning in Spark is a transformation that redistributes the data across a new set of partitions. It is used when you need to increase or decrease the number of partitions to balance the workload, improve parallelism, or prepare for a join or aggregation that benefits from a different partitioning scheme. Repartitioning triggers a full shuffle, which can be expensive, so it should be used when the benefits outweigh the cost. For example, after filtering a large dataset to a smaller subset, you might repartition to reduce the number of partitions and avoid small file problems. Conversely, before a wide join, you might repartition the smaller dataset to match the partitioning of the larger one to reduce shuffle. Repartitioning is different from coalesce, which only reduces partitions without a shuffle. Use repartition when you need a new partitioning scheme or to increase parallelism; use coalesce when you only need to reduce partitions.