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Describe a custom EMR cluster configuration for Spark-based ETL with minimal cost.

Spark/Big Dataeasy0.3 min read

**Why config matters**: 50–70% cost savings with right instance mix. **Minimal-cost config**: (1) Spot instances for workers (interruptible); (2) On-demand for driver (reliability). (3) Right-size: m5.xlarge or m5.2xlarge workers; 1 driver, 2–10 workers. (4) S3 for storage (no...

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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
Capco
Key Concepts Tested
etlspark

Why This Question Matters

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

How to Approach This

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
70 words

Why config matters: 50–70% cost savings with right instance mix. Minimal-cost config: (1) Spot instances for workers (interruptible); (2) On-demand for driver (reliability). (3) Right-size: m5.xlarge or m5.2xlarge workers; 1 driver, 2–10 workers. (4) S3 for storage (no EBS for data). (5) Autoscaling. Scalability trade-offs: Spot = possible interruption; use checkpointing. Cost implications: Spot ~70% cheaper; appropriate instance types. Best practice: Spot for batch; on-demand driver; S3 lifecycle; monitor costs.

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