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Home/Questions/Python/Coding/How would you handle memory constraints when processing a large dataset in Python?

How would you handle memory constraints when processing a large dataset in Python?

Python/Codinghard0.1 min readPremium

**Strategies:** (1) Chunked read. (2) Dtypes—category, int8/16/32. (3) Generators. (4) Dask/Vaex—out-of-core. (5) Disk/SQL. (6) Streaming. **Why:** Fit in memory. **Production:** memory_profiler. **Scalability:** Chunk + aggregate.

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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
Goldman Sachs
Key Concepts Tested
pythonsql

Why This Question Matters

This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Goldman Sachs. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python, sql) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
25 words

Strategies: (1) Chunked read. (2) Dtypes—category, int8/16/32. (3) Generators. (4) Dask/Vaex—out-of-core. (5) Disk/SQL. (6) Streaming. Why: Fit in memory. Production: memory_profiler. Scalability: Chunk + aggregate.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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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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