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

Spark/Big Datahard0.4 min read

**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)...

🤖 Analyze Your Answer
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

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
77 words

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. Network and CPU limits.

Cost Implications: Sub-second often 2–3x cost of minute-level. Set SLO; optimize to that.

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