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. Adaptive Query Execution (AQE) optimizes Spark queries at runtime. Introduced in Spark 3.x, it: (1) Coalesces partitions after shuffle...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Globant. 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.
Adaptive Query Execution (AQE) optimizes Spark queries at runtime. Introduced in Spark 3.x, it: (1) Coalesces partitions after shuffle based on actual data sizes (spark.sql.adaptive.coalescePartitions.enabled). (2) Converts Sort-Merge Join to Broadcast Join when runtime stats show a join side is small. (3) Handles skewed joins by splitting large partitions. Enable with spark.sql.adaptive.enabled=true. Benefits: Fewer manual tuning; better handling of data skew; reduced shuffle partitions. Best practice: Combine with spark.sql.adaptive.skewJoin.enabled and spark.sql.adaptive.advisoryPartitionSizeInBytes for production.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Disabling AQE without reason. Pro-Move: 'AQE + skewJoin + coalesce; measure impact.'
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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.