Dynamic partition pruning: Optimizer uses runtime filter values to skip partitions. E.g., JOIN fact table with dimension filtered by date; the date filter is pushed to the fact scan, so only matching partitions are read. In Spark: Enabled by default with AQE;...
This medium-level SQL question appears frequently in data engineering interviews at companies like TCS. 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.
Dynamic partition pruning: Optimizer uses runtime filter values to skip partitions. E.g., JOIN fact table with dimension filtered by date; the date filter is pushed to the fact scan, so only matching partitions are read. In Spark: Enabled by default with AQE; broadcast-small-dimension pattern. Effect: Fewer I/O, faster scans. Example: fact partitioned by date; query filters dim by region and joins—Spark can prune fact partitions if correlation allows. Best practice: Partition by common filter columns; use stats for the optimizer. Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.