DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/What is the difference between MapReduce and Spark?

What is the difference between MapReduce and Spark?

Spark/Big Datahard0.5 min read

**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...

🤖 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
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.

dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech
dataengprep.techdataengprep.techdataengprep.techdataengprep.tech

Want feedback on your answer?

Paste your answer to this question and our AI Coach scores it, finds gaps, and shows you the FAANG-level version.

Try Answer Analyzer →
Want all answers as a PDF for offline study?
1,863 questions across 7 categories — Interview Packs →

Free: Top 20 SQL Interview Questions (PDF)

Get the most asked SQL questions with expert answers. Instant download.

No spam. Unsubscribe anytime.

Related Spark/Big Data Questions

mediumWhat is the difference between repartition and coalesce in Apache Spark?FreehardWhat is the difference between SparkSession and SparkContext in Spark?FreemediumWhat is the difference between cache() and persist() in Spark? When would you use each?FreemediumWhat is the difference between groupByKey and reduceByKey in Spark?FreemediumWhat is the difference between narrow and wide transformations in Apache Spark? Explain with examples.Free

Companies that ask this Spark/Big Data question

Globant interview questions →

Want to know if YOUR answer is good enough?

Paste your answer and get instant AI feedback with a FAANG-level improved version.

Analyze My Answer — Free

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.

← Back to all questionsMore Spark/Big Data questions →