How do you optimize Spark jobs (partitioning, shuffles, caching)?

Spark performance depends on minimizing shuffles and choosing good partitioning. Key tactics: - Partition by common filter/join keys - Reduce wide transformations - Use broadcast joins when appropriate - Cache only reused datasets Profile jobs with Spark UI and tune based on skew, shuffle size, and executor utilization.