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. Cores vs Executors: Executor = JVM process on worker; runs tasks. Cores = CPU threads per executor (parallel tasks per executor)....
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Dunnhumby. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, spark) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Cores vs Executors: Executor = JVM process on worker; runs tasks. Cores = CPU threads per executor (parallel tasks per executor). Example: 10 executors, 4 cores each = 40 parallel tasks. Trade-off: More executors = more parallelism but more overhead. More cores = fewer executors, less overhead but potential contention. Best practice: 5-7 cores per executor; leave 1 core for OS; total cores ~2-4x cluster CPUs.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Too many cores per executor. Pro-Move: '5-7 cores; 1 for OS; profile.'
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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.