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What strategies would you use to reduce latency in a streaming data pipeline?

Spark/Big Datahard0.4 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
BCG
Key Concepts Tested
partition
Expert AnswerPremium
77 wordsInterview-ready
**Strategies**: (1) More parallelism (partitions, executors). (2) Smaller micro-batches (trigger interval). (3) Efficient serialization (Avro, Protobuf). (4) Co-locate consumers and brokers. (5) Pipeline stages (overlap read/process/write). (6) Avoid unnecessary shuffle. (7) Continuous processing if supported. **Why Trade-offs**: Lower latency = smaller batches = more overhead. Higher throughput = larger batches. Match to SLA. **Scalability Trade-offs**: More partitions = more parallelism....
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 BCG. The answer also includes follow-up discussion points that interviewers commonly explore.

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