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Compare Spark's lineage recovery with Hadoop's block replication mechanism.

Spark/Big Datamedium0.5 min readPremium

**Why this matters**: Different fault-tolerance philosophies—compute vs storage. **Spark**: Lineage-based recovery. Lost partition = recompute from RDD lineage (transformations). No data replication; trades storage for compute. **Hadoop**: Block replication (default 3x)....

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Asked at 1 company

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Impetus. 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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Expert Answer

92 words

Why this matters: Different fault-tolerance philosophies—compute vs storage. Spark: Lineage-based recovery. Lost partition = recompute from RDD lineage (transformations). No data replication; trades storage for compute. Hadoop: Block replication (default 3x). Data-centric; node failure = read from replica. Scalability trade-offs: Spark = less storage, more recompute on failure; Hadoop = more storage, fast recovery. Long lineage = slow recovery; checkpoint truncates. Cost implications: Spark = cheaper storage, recompute cost on failure; Hadoop = 3x storage cost. Best practice: Checkpoint long lineages; use reliable storage (S3) for shuffle; consider replication for hot data.

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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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