Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**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....
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.
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.
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.
Pro-Move: 'All batch jobs on transient + spot; fleet manager for reliability.' Red Flag: Long-running cluster for nightly job—paying for 20h idle.
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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 an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.