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Home/Questions/Spark/Big Data/Share your experience in working with big data technologies such as Hadoop, Spark, or AWS EMR. How have you leveraged these tools in your previous projects?

Share your experience in working with big data technologies such as Hadoop, Spark, or AWS EMR. How have you leveraged these tools in your previous projects?

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
Amazon
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
114 wordsInterview-ready
**Situation**: E-commerce analytics pipeline processing 50TB daily faced 24h batch latency; stakeholders demanded near-real-time dashboards. **Task**: Lead migration from legacy Hadoop to Spark on EMR while maintaining zero data loss and improving SLA from 24h to sub-4h. **Action**: (1) Architected Spark-structured streaming with Kafka for incremental ingestion; (2) Implemented Delta Lake for ACID and upserts; (3) Tuned partitions, broadcast joins, AQE; (4) Established monitoring and runbooks....
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 Amazon. The answer also includes follow-up discussion points that interviewers commonly explore.

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