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Data-Related Issues Encountered - handling skewed data

Spark/Big Datamedium0.4 min readPremium

**Why skew matters**: Uneven partition sizes = stragglers; one partition 10x others = 90% resources idle. **Resolutions**: (1) **Salting**—add random suffix to key, redistribute, aggregate, then merge. (2) **Broadcast**—small dimension, avoid shuffle. (3) **Split hot...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Lumiq
Key Concepts Tested
partitionspark

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This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Lumiq. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark) will help you answer variations of this question confidently.

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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.

Expert Answer
81 words

Why skew matters: Uneven partition sizes = stragglers; one partition 10x others = 90% resources idle. Resolutions: (1) Salting—add random suffix to key, redistribute, aggregate, then merge. (2) Broadcast—small dimension, avoid shuffle. (3) Split hot keys—e.g., user_id 12345 → 12345_0, 12345_1. (4) AQE—Spark 3.x can detect skew. Scalability trade-offs: Salting adds complexity; broadcast has size limit. Cost implications: Skew = 2–5x wasted compute; fixing = significant savings. Best practice: Identify via Spark UI (task duration variance); apply salting; monitor partition sizes.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data 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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