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. EMR fault tolerance: (1) Persist to S3 (durable); (2) Spot instances plus checkpoint; (3) Retry failed steps; (4) Use HA for HDFS if...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
EMR fault tolerance: (1) Persist to S3 (durable); (2) Spot instances plus checkpoint; (3) Retry failed steps; (4) Use HA for HDFS if needed; (5) Enable Spark checkpoint for streaming. Best practice: design for recompute; use spot for task nodes; configure retries; avoid single points of failure; test failover.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Single point of failure. Pro-Move: 'S3 durable; spot + retry; checkpoint.'
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.