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What are transient clusters in EMR, and when would you use them?

Spark/Big Dataeasy0.5 min readPremium

**Transient Cluster**: Runs only during job execution; terminates when job completes. No idle time. **When to Use**: Batch ETL, scheduled jobs, sporadic workloads. Pay only for job duration. **Why vs. Long-Running**: Long-running = pay 24/7. Transient = pay for 2–8 hours....

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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
Persistent Systems
Key Concepts Tested
etl

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Persistent Systems. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl) will help you answer variations of this question confidently.

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Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
100 words

Transient Cluster: Runs only during job execution; terminates when job completes. No idle time.

When to Use: Batch ETL, scheduled jobs, sporadic workloads. Pay only for job duration.

Why vs. Long-Running: Long-running = pay 24/7. Transient = pay for 2–8 hours. 60–80% cost savings for batch.

Implementation: Create cluster with Step; set "Action on Failure" to terminate. Cluster shuts down after last step.

Scalability Trade-offs: Startup time (5–10 min) adds to each run. Use for idempotent jobs—restartable on failure.

Cost Implications: Spot instances within transient = additional 60–70% savings. For daily batch, transient + spot = 90% cheaper than always-on.

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