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What is the difference between MapReduce and Spark?

Spark/Big Datahard0.5 min readPremium

**MapReduce**: Disk-based. Each job writes to HDFS between map and reduce. Multi-stage = multiple disk I/O. Latency: minutes to hours per stage. **Spark**: In-memory (when cached). DAG with lazy evaluation. RDD/DataFrame caching. 10–100x faster for iterative (ML, graph). **Why...

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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
Globant
Key Concepts Tested
spark

Why This Question Matters

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

MapReduce: Disk-based. Each job writes to HDFS between map and reduce. Multi-stage = multiple disk I/O. Latency: minutes to hours per stage.

Spark: In-memory (when cached). DAG with lazy evaluation. RDD/DataFrame caching. 10–100x faster for iterative (ML, graph).

Why Spark Won: Iterative algorithms (e.g., PageRank, K-means) run 10–100x faster. Unified batch + streaming. DataFrame API + Catalyst.

Scalability Trade-offs: Spark needs more memory. MapReduce more fault-tolerant per stage (writes to disk). Spark handles failures via lineage.

Cost Implications: Spark = fewer cluster hours for same work. MapReduce for legacy or disk-bound workloads.

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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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