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Walk through how you would debug the data ingestion process to identify slow stages.

Spark/Big Datahard0.6 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
Swiggy
Key Concepts Tested
partitionspark
Expert AnswerPremium
112 wordsInterview-ready
**Situation**: Ingestion job exceeds SLA; need to find and fix bottleneck. **Task**: Systematically isolate slow stage (source, transfer, or Spark). **Action**: (1) **Add timestamps**—log start/end per stage; identify largest delta. (2) **Spark UI**—Jobs/Stages: which stage dominates? Task duration histogram shows skew. (3) **Source checks**—DB: connection pool exhaustion? Network latency? Lock contention? (4) **Sqoop/Kafka**—Sqoop: `--num-mappers`, `--fetch-size`, `--split-by`....
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 Swiggy. The answer also includes follow-up discussion points that interviewers commonly explore.

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