How do you address data skew in Spark, and what remediation steps would you follow?

Data skew occurs when a few keys dominate the data, causing some partitions to be much larger than others. To mitigate it:

1. Salting – add a random prefix to skewed keys before the join, then group by the original key after the join.
2. Broadcast join – if one side is small, broadcast it to all executors to avoid shuffling.
3. Repartitioning – use repartition or coalesce with a custom partitioner to spread data more evenly.
4. Skew hints – in Hive or Spark SQL, use SET spark.sql.adaptive.skewJoin.enabled=true to let Spark automatically handle skew.
5. Custom partitioner – implement a partitioner that balances load based on key frequency.
6. Sampling – analyze data distribution first to identify skewed keys.

These steps reduce the time spent on the heavy partition and improve overall job performance.