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Running Tasks in Parallel

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
Tech Mahindra
Key Concepts Tested
airflowoptimizationpartitionspark
Expert AnswerPremium
79 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Running tasks in parallel: In Spark—stages run sequentially; tasks within stage run in parallel. In Airflow—set `parallelism`; tasks in different branches run in parallel. In Databricks—multiple jobs can run on different clusters....
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 Tech Mahindra. The answer also includes follow-up discussion points that interviewers commonly explore.

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