DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/Spark/Big Data/Describe a custom EMR cluster configuration for Spark-based ETL with minimal cost.

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

Spark/Big Dataeasy0.3 min readPremium

**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...

🤖 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
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.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

This answer is partially locked

Unlock the full expert answer with code examples and trade-offs

Recommended

Start AI Mock Interview

Practice real interviews with AI feedback, track progress, and get interview-ready faster.

  • Unlimited AI mock interviews
  • Instant feedback & scoring
  • Full answers to 1,800+ questions
  • Resume analyzer & SQL playground
Create Free Account

Pro starts at $24/mo - cancel anytime

Just need answers for quick revision?

Download curated PDF interview packs

Interview Packs
1,800+ real interview questions sourced from 5 top companies
AmazonGoogleDatabricksSnowflakeMeta
This answer is in the DE Mastery Vault 2026
1,863 questions with expert answers across 7 categories →

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

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 →