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How would you design a scalable and fault-tolerant data processing pipeline for handling large volumes of streaming data?

Spark/Big Datahard2.6 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
Amazon
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
511 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Fault-tolerant streaming: backpressure, replication, checkpoint, DLQ. No DLQ—one bad record blocks batch. No circuit breaker allows cascading failures. **Section 2 — The Diagram** ``` [Sources] --> [Kinesis | Kafka] | v [Spark Streaming] Checkpoint | DLQ | v [Delta | S3] Replication ``` **Section 3 — Component Logic** **Kinesis/Kafka** durable buffer; RF=3. **Checkpoint** enables replay. **DLQ** for poison messages....
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 Amazon. The answer also includes follow-up discussion points that interviewers commonly explore.

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