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Compare compression algorithms: Gzip vs Snappy.

Python/Codingeasy0.2 min read

**Gzip:** Higher ratio (2–3x), 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....

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in Python/Coding
Difficulty Split
127E|24M|28H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Gartner
Interview Pro Tip

Pro-Move: Zstd benchmark for your workload. Red Flag: Defaulting without profiling.

Key Concepts Tested
spark

Why This Question Matters

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.

How to Approach This

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.

Expert Answer
41 words

Gzip: Higher ratio (2–3x), 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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According 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.

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