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How would you design a Kafka-based pipeline for processing streaming data in real-time?

Spark/Big Datahard2.5 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
Citi
Key Concepts Tested
optimizationpartitionsparkwindow
Expert AnswerPremium
503 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Kafka-based real-time pipeline must achieve exactly-once. Windowing, enrichment, idempotent sink. At-least-once with non-idempotent sink causes duplicates. **Section 2 — The Diagram** ``` [Kafka] --> [Consumer] Partitions | Keys | v [Spark Stream] Windowing | Enrich | v [Delta] Idempotent Merge ``` **Section 3 — Component Logic** **Kafka** partitioned streams. **Spark Stream** windowing, enrichment....
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 Citi. The answer also includes follow-up discussion points that interviewers commonly explore.

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