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Explain how Glue's Spark-based architecture handles data parallelism.

Spark/Big Datahard3.5 min readPremium
Frequency
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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
Capco
Key Concepts Tested
etloptimizationpartitionspark
Expert AnswerPremium
699 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** AWS Glue runs Spark jobs on serverless DPUs—each DPU provides 4 vCPUs and 16GB RAM. The challenge is that Glue abstracts away cluster management, so engineers often treat it as a black box and miss parallelism tuning. Small file problems, incorrect partition counts, and wrong worker types cause jobs to run 5–10x slower than necessary....
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 Capco. The answer also includes follow-up discussion points that interviewers commonly explore.

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