**Architectural Logic**: DPP in Spark pushes dimension filters to fact table to skip partitions—type mismatch or missing broadcast breaks it. **Mechanism**: When dimension is filtered and broadcast, Spark pushes partition filter to fact. **Error Causes**: Partition column type...
This medium-level SQL 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, partition, spark) will help you answer variations of this question confidently.
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Architectural Logic: DPP in Spark pushes dimension filters to fact table to skip partitions—type mismatch or missing broadcast breaks it. Mechanism: When dimension is filtered and broadcast, Spark pushes partition filter to fact. Error Causes: Partition column type mismatch (e.g., INT vs STRING); filter not pushed; broadcast hint missing. Fix: (1) Align join key types with partition column. (2) broadcast(dim_df) for small dimension. (3) Enable spark.sql.optimizer.dynamicPartitionPruning.enabled (default true). (4) Ensure partition column in join condition. Scalability: DPP reduces I/O significantly; failure = full scan. Best Practice: Check query plan for DPP; align types; use broadcast for dimensions.
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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.