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Home/Questions/Spark/Big Data/Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.

Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.

Spark/Big Datahard2.9 min readPremium
Frequency
Low
Asked at 2 companies
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
LTIMindtreePWC
Key Concepts Tested
joinoptimizationpartition
Expert AnswerPremium
573 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Databricks workload cost explodes when clusters run idle, jobs are over-provisioned, or spot preemption causes thrashing. The challenge is aligning DPU allocation to actual parallelism while maintaining SLA....
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 LTIMindtree, PWC. The answer also includes follow-up discussion points that interviewers commonly explore.

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