Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark partitioning: data is divided into partitions; each partition maps to a task. Default partition count from input or 200. Shuffle...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Uber. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization, partition) 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.
Spark partitioning: data is divided into partitions; each partition maps to a task. Default partition count from input or 200. Shuffle repartitions by key (hash or range). Shuffles are expensive—network and disk I/O. Tuning: (1) Increase partitions for large shuffles; (2) Reduce partitions after filter via coalesce; (3) Partition on join keys to avoid shuffle; (4) Use broadcast for small tables. Best practice: aim for partition size 100–200MB; monitor shuffle read/write in Spark UI; use repartition(n) or repartition('key') before expensive aggregations.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Default 200 partitions always. Pro-Move: '100–200MB/partition; monitor shuffle in UI.'
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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.