Redshift compression benefits: Reduced storage, less I/O, faster scans, lower cost. Encodings: AZ64, LZO, ZSTD, etc. Drawbacks: (1) CPU overhead for encode/decode—can slow writes. (2) Random access—compressed blocks must be decompressed. (3) Wrong encoding—poor choice can...
This easy-level SQL question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Redshift compression benefits: Reduced storage, less I/O, faster scans, lower cost. Encodings: AZ64, LZO, ZSTD, etc. Drawbacks: (1) CPU overhead for encode/decode—can slow writes. (2) Random access—compressed blocks must be decompressed. (3) Wrong encoding—poor choice can increase size (e.g., RLE on high-cardinality). Best practice: Use AUTO encoding for new tables; analyze ANALYZE COMPRESSION recommendations. Choose BYTEDICT for low-cardinality; DELTA for timestamps. Monitor compressibility and query performance post-encoding. 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.