**Approach**: (1) Execution plan—identify full scans, shuffle size, sort. (2) Partition pruning—filter on partition key (e.g., dt) before scan. (3) Predicate pushdown—ensure filters reach storage layer. (4) Index/Clustering—B-tree on filter; Z-order on multi-column. (5) Reduce...
This medium-level SQL question appears frequently in data engineering interviews at companies like Goldman Sachs. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) 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.
Approach: (1) Execution plan—identify full scans, shuffle size, sort. (2) Partition pruning—filter on partition key (e.g., dt) before scan. (3) Predicate pushdown—ensure filters reach storage layer. (4) Index/Clustering—B-tree on filter; Z-order on multi-column. (5) Reduce scope—narrow columns; APPROX_COUNT_DISTINCT when exact not needed. (6) Incremental—process only new/changed data. (7) Materialized aggregation—pre-aggregate for frequent patterns. At scale: Unpruned scan of 1B rows = 100GB+ I/O; partitioned + columnar = 1% scan.
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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.