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Setting Dependencies for Tasks in DAG

Spark/Big Datahard0.3 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
70 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Task dependencies in DAG: In Airflow: `task2.set_upstream(task1)` or `task1 >> task2`. Define order: extract → transform → load. In Spark: Stages imply dependency. Best practice: Minimize depth; parallelize independent branches. **Scalability trade-offs**: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling....
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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