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How would you optimize cost when using AWS for large-scale data processing?

Cloud/Toolsmedium0.7 min readPremium
Frequency
Low
Asked at 1 company
Category
179
questions in Cloud/Tools
Difficulty Split
104E|27M|48H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Wipro
Key Concepts Tested
partition
Expert AnswerPremium
132 wordsInterview-ready
**Architectural cost logic**: At scale, 10–20% savings = millions. Prioritize by spend: compute (Glue/EMR/Lambda), storage (S3), and data transfer. **Compute**: Right-size—Glue bills by DPU-minute (10 DPU minimum); profile jobs and start at 10, scale only if I/O-bound. Use Spot for Glue/EMR where fault-tolerance exists (checkpoints, idempotent writes); we achieved 40% EMR savings. Reserved Instances/Savings Plans for steady-state (e.g., always-on clusters)....
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 Wipro. The answer also includes follow-up discussion points that interviewers commonly explore.

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