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Conceptualize and design a real-time streaming data pipeline end-to-end.

Spark/Big Datahard4 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
Expedia
Key Concepts Tested
joinoptimizationpartitionsparksqlwindow
Expert AnswerPremium
805 wordsIncludes code examplesInterview-ready
**Section 1 — The Context (The 'Why')** Real-time streaming pipelines face a fundamental tension: events arrive continuously at high velocity while downstream consumers demand low latency, yet the system must guarantee no data loss during broker failures or consumer restarts. A naive approach—writing directly to a database per event—collapses under load; checkpointing to local disk loses state on executor preemption....
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 Expedia. The answer also includes follow-up discussion points that interviewers commonly explore.

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