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What trade-offs would you consider when choosing between batch processing and real-time streaming?

Spark/Big Datahard0.4 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
McKinsey
Key Concepts Tested
partition
Expert AnswerPremium
77 wordsInterview-ready
**Latency**: Batch hours; stream seconds. **Throughput**: Batch higher per dollar; stream lower. **Complexity**: Batch simpler; stream more ops. **Cost**: Batch cheaper per GB; stream always-on. **When Batch**: Analytics, ML training, reporting. Latency acceptable (hours). **When Stream**: Alerts, fraud, real-time dashboards. SLA = seconds/minutes. **Hybrid**: Lambda (stream + batch) or incremental batch (hourly micro-batch). **Scalability Trade-offs**: Batch scales with partition count....
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 McKinsey. The answer also includes follow-up discussion points that interviewers commonly explore.

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