Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)?

AQE runs at shuffle boundaries: recalculates partition counts, join strategies, and skew using runtime statistics. Features: (1) Coalesce shuffle partitions—merge small partitions post-shuffle, fewer tasks. (2) Switch sort-merge to broadcast when stats show small side. (3) Skew join—split oversized partitions. Why it matters economically: reduces manual tuning—fewer hours on config, fewer job failures from bad stats....