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Compare Hadoop and Spark. Which one would you choose for a real-time application, and why?

Spark/Big Datahard0.4 min readPremium

**Why choice matters**: Hadoop MapReduce = disk-bound, batch; Spark = in-memory, streaming capable. **Hadoop (MapReduce)**: Disk I/O between stages; high latency; batch-oriented. **Spark**: In-memory; lazy; Structured Streaming for micro-batch/continuous. **For real-time**:...

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Asked at 1 company

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like BCG. 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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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

83 words

Why choice matters: Hadoop MapReduce = disk-bound, batch; Spark = in-memory, streaming capable. Hadoop (MapReduce): Disk I/O between stages; high latency; batch-oriented. Spark: In-memory; lazy; Structured Streaming for micro-batch/continuous. For real-time: Choose Spark—Structured Streaming, sub-second to minute latency. Hadoop suits cold archival, cost-optimized batch. Scalability trade-offs: Spark streaming scales with partitions; Hadoop batch scales with cluster. Cost implications: Real-time = more resources; Spark more efficient per unit compute. Best practice: Spark for real-time and interactive; Hadoop for cold storage if already in use.

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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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