How would you optimize Spark jobs?

Optimizing Spark jobs involves several layers of tuning. First, ensure data is partitioned appropriately: use a partition key that aligns with downstream operations to avoid shuffles. Use the right number of partitions—too few leads to large tasks, too many increases overhead. Cache or persist intermediate RDDs/DataFrames only when they are reused, and choose the storage level (MEMORY_ONLY, MEMORY_AND_DISK, etc.) based on memory availability. Broadcast small lookup tables to avoid costly shuffle joins. Leverage Tungsten and Catalyst optimizations by using DataFrame/Dataset APIs instead of low-level RDDs. Enable dynamic allocation to scale executors based on workload. Tune executor memory and cores: a common rule is 1 core per 2–4 GB of memory. Use the Spark UI to identify long stages and narrow transformations. Avoid wide transformations like groupByKey; prefer reduceByKey or aggregateByKey. For skewed data, use salting or custom partitioners. Finally, monitor GC overhead and consider using G1GC or ZGC for large heaps. Each of these steps reduces execution time, memory usage, and improves overall cluster utilization.