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Trade-offs between batch processing (Spark) vs. real-time streams (Kafka)

Spark/Big Datahard0.7 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
PayPal
Key Concepts Tested
partitionspark
Expert AnswerPremium
136 wordsInterview-ready
**Why Choose One Over the Other**: Latency SLA drives the decision. Analytics and ML training tolerate hours; fraud detection and alerts need seconds. **Batch (Spark)**: (1) Higher throughput per dollar—sequential reads, bulk writes. (2) Simpler failure model—rerun from last checkpoint. (3) Lower ops overhead—no 24/7 cluster. (4) Latency: minutes to hours. **Streaming (Kafka + Spark/Flink)**: (1) Sub-second to minute latency. (2) Event-driven; backpressure. (3) Exactly-once adds complexity....
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 PayPal. The answer also includes follow-up discussion points that interviewers commonly explore.

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