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Home/Questions/Spark/Big Data/Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.

Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.

Spark/Big Datahard0.7 min readPremium
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Asked at 2 companies
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AltimetrikInfosys
Key Concepts Tested
spark
Expert AnswerPremium
132 wordsInterview-ready
Hadoop's architecture centers on HDFS (storage) + YARN (resource scheduling) + MapReduce (compute model). Components: Namenode (single point for metadata—scalability ceiling for namespace); Datanodes (block storage, 128MB default blocks, 3x replication); ResourceManager/NodeManagers for CPU/memory allocation. Why it matters architecturally: HDFS scales linearly with nodes but metadata scale is bounded by Namenode heap; YARN allows multi-tenant workloads but adds operational overhead....
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 Altimetrik, Infosys. The answer also includes follow-up discussion points that interviewers commonly explore.

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