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Alternatives to the Medallion Architecture

Spark/Big Datahard3.6 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
KPMG
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
724 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** The primary challenge for this design in Spark/Big Data is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....
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 KPMG. The answer also includes follow-up discussion points that interviewers commonly explore.

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