Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Situation**: Production join job on 10B-row fact table with 100M-row dimension timed out; one task ran 2h vs 5min for others. **Task**: Identify root cause and fix without schema changes. **Action**: (1) Profiled via Spark UI—identified skew on region key (80% data in 3...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like S&P Global. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, spark) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Situation: Production join job on 10B-row fact table with 100M-row dimension timed out; one task ran 2h vs 5min for others. Task: Identify root cause and fix without schema changes. Action: (1) Profiled via Spark UI—identified skew on region key (80% data in 3 partitions); (2) Implemented salting: replicated dimension with salt range, joined on (key, salt), aggregated; (3) Enabled AQE skew join as fallback. Result: P99 dropped 70%; job met SLA; pattern documented for team. Detail: OOM—increased partitions, broadcast small; slow query—Z-order on filter columns, partition pruning.
Scalability trade-offs: Salting adds shuffle overhead; AQE adaptive. Cost implications: Right-size after fix.
Red Flag: Generic challenge. Pro-Move: 'Specific; quantify; skew/OOM/slow; solution.'
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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.