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Production Experience - deploying and monitoring Spark jobs

Spark/Big Datahard0.3 min readPremium
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
LTIMindtree
Key Concepts Tested
airflowoptimizationpartitionspark
Expert AnswerPremium
69 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Production Spark: Deploy via spark-submit, Airflow, or Databricks Jobs. Monitor: Spark UI, logs, metrics. Alert on failure. Use retries, backoff. Best practice: Idempotent jobs; version control; staging first; runbooks. **Scalability trade-offs**: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like LTIMindtree. The answer also includes follow-up discussion points that interviewers commonly explore.

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