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Fault Tolerance in Spark vs. Hadoop?

Spark/Big Datahard0.5 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark fault tolerance: lineage-based—recompute lost partitions from RDD graph; in-memory caching speeds recovery. Hadoop:...

🤖 Analyze Your Answer
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
Capco
Key Concepts Tested
optimizationpartitionspark

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Capco. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition, spark) 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
90 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Spark fault tolerance: lineage-based—recompute lost partitions from RDD graph; in-memory caching speeds recovery. Hadoop: replication-based—replicate blocks; re-read on failure. Spark is faster for iterative workloads due to in-memory and lineage. Both handle node failure. Best practice: persist critical intermediates; use checkpoint for long lineages; ensure sufficient replication in storage layer.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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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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