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Defining Tasks in DAG

Spark/Big Dataeasy0.3 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
Verizon
Key Concepts Tested
airflowpython
Expert AnswerPremium
65 wordsInterview-ready
**Why task design matters**: Clear tasks = debuggable, testable, resumable. **In Airflow**: Tasks = operators (PythonOperator, BashOperator, etc.). Define with `task_id`. Dependencies: `task_a >> task_b` or `task_a.set_downstream(task_b)`. TaskGroup for grouping. **Scalability trade-offs**: Too many small tasks = overhead; too few = coarse retries. **Cost implications**: Task overhead = DB/worker time; balance granularity....
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 Verizon. The answer also includes follow-up discussion points that interviewers commonly explore.

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