Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark optimization: Broadcast (<10MB), caching (reuse), coalesce (reduce partitions), predicate pushdown (Parquet), AQE (adaptive). Best...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Spark optimization: Broadcast (<10MB), caching (reuse), coalesce (reduce partitions), predicate pushdown (Parquet), AQE (adaptive). Best practice: Apply based on bottleneck; measure impact.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Apply all without measure. Pro-Move: 'Bottleneck first; measure impact.'
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