**Gzip:** Higher ratio (2β3x), slower (CPU). **Snappy:** Faster compress/decompress, lower ratio. **When:** Gzip cold storage/archival; Snappy hot/Spark. **Parquet:** Snappy default; good balance. **Why:** Match to workload. **Trade-off:** Zstd for better ratio + speed....
Pro-Move: Zstd benchmark for your workload. Red Flag: Defaulting without profiling.
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Gartner. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.
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.
Gzip: Higher ratio (2β3x), slower (CPU). Snappy: Faster compress/decompress, lower ratio. When: Gzip cold storage/archival; Snappy hot/Spark. Parquet: Snappy default; good balance. Why: Match to workload. Trade-off: Zstd for better ratio + speed. Cost: CPU vs storage; benchmark on your data.
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Analyze My Answer β FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding 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.