How can you resolve data skewing issues in Spark when performing joins?

Data skew occurs when a few keys dominate the join, causing some tasks to process far more data than others. To mitigate skew: 1) Salting – add a random suffix to the skewed key before the join and then group by the original key after the join. 2) Broadcast the small side of the join if it fits in memory. 3) Use repartition or coalesce to increase parallelism for the skewed partition. 4) Enable Spark’s spark.sql.adaptive.enabled and spark.sql.adaptive.skewJoin.enabled to let the engine automatically handle skew. 5) For very large skewed keys, consider custom partitioners or map-side combine logic. Each technique trades off complexity, memory usage, and performance, so choose based on the size of the skew and cluster resources.