Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Small files in S3 cause Redshift COPY slowdowns (each file triggers a slice). Solutions: (1) Coalesce before load—run a Spark/Glue job to merge files (e.g., 128MB per file). (2) Use manifest files—COPY from a manifest listing fewer, larger files. (3) Enable MANIFEST in Glue/ETL...
This medium-level SQL question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, 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.
Small files in S3 cause Redshift COPY slowdowns (each file triggers a slice). Solutions: (1) Coalesce before load—run a Spark/Glue job to merge files (e.g., 128MB per file). (2) Use manifest files—COPY from a manifest listing fewer, larger files. (3) Enable MANIFEST in Glue/ETL to output fewer parts. (4) Buffer in Kinesis Firehose with size/count thresholds. (5) Use Redshift Spectrum for ad-hoc S3 queries without loading. Best practice: target 1–128MB files per Redshift slice. Example Spark: df.coalesce(num_files).write.parquet("s3://bucket/prefix/"). Glue: use 'glueparquet' with write_dynamic_frame and reducePartitions. Redshift: COPY orders FROM 's3://bucket/prefix/' MANIFEST; 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.
Red Flag: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.