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

Spark/Big Datahard0.4 min read

**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)....

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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

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like McKinsey. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
77 words

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. Stream with Kafka partitions.

Cost Implications: Match to SLA. Over-streaming = 2–3x cost. Incremental batch = compromise.

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