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How would you optimize your Spark Streaming ETL pipeline for high throughput and low latency?

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
Meesho
Key Concepts Tested
etloptimizationpartitionsparksql
Expert AnswerPremium
86 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Optimize Spark Streaming ETL: (1) Micro-batch size—`spark.sql.shuffle.partitions`; adjust `maxFilesPerTrigger`. (2) Parallelism—multiple Kafka partitions, adequate executors. (3) Avoid shuffle—use state wisely; broadcast static data. (4) Sink—batch writes; use foreachBatch for Delta merge. (5) Checkpoint—efficient location....
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 Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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