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What are the challenges of implementing real-time analytics using Spark Streaming?

Spark/Big Datahard0.5 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Goldman Sachs
Key Concepts Tested
partitionsparkwindow
Expert AnswerPremium
97 wordsInterview-ready
**Why Challenging**: Micro-batch (Structured Streaming) has inherent latency (trigger interval); true real-time requires sub-ms. State and exactly-once add complexity. **Challenges**: (1) **Latency**—micro-batch = seconds; continuous processing still limited. (2) **Backpressure**—slow sink causes backlog; configure `maxOffsetsPerTrigger`. (3) **Checkpoint**—required for exactly-once; deleted checkpoint = full reprocess or offset reset. (4) **State**—windowed agg state grows; use state timeout....
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 Goldman Sachs. The answer also includes follow-up discussion points that interviewers commonly explore.

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