Suppose you have a join where one customer ID accounts for about 40% of the entire dataset. What problems can arise from this skew?

When a single key dominates a join, the tasks that process that key become bottlenecks. In Spark, each partition processes a subset of keys; if one key appears in 40% of the data, the partition containing that key will have far more rows than others, causing uneven task execution times. This leads to stragglers, increased shuffle traffic, higher memory usage, and potential out‑of‑memory errors. The overall job can take much longer than expected. To mitigate skew, you can: 1) use a skew‑aware join by adding a random suffix to the skewed key and broadcasting the smaller side; 2) repartition the data with a custom partitioner that balances load; 3) use Spark's spark.sql.adaptive.skewJoin.enabled to let AQE handle it; 4) pre‑aggregate or filter the skewed key separately. Monitoring the Spark UI for stage durations and task metrics helps identify skew early.