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Home/Questions/Spark/Big Data/What is a DAG in Apache Airflow, and how is it used for scheduling workflows?

What is a DAG in Apache Airflow, and how is it used for scheduling workflows?

Spark/Big Dataeasy0.4 min read

**DAG**: Directed Acyclic Graph. Tasks = nodes; dependencies = edges. No cycles. Defines workflow. **Scheduling**: Airflow parses DAG; respects dependencies; runs task when upstream succeed. Enables parallelism (independent tasks), retries, monitoring. **Why DAG**: Explicit...

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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
Citi
Key Concepts Tested
airflow

Why This Question Matters

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

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Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.

Expert Answer
73 words

DAG: Directed Acyclic Graph. Tasks = nodes; dependencies = edges. No cycles. Defines workflow.

Scheduling: Airflow parses DAG; respects dependencies; runs task when upstream succeed. Enables parallelism (independent tasks), retries, monitoring.

Why DAG: Explicit ordering; clear data flow. Acyclic = no infinite loops.

Scalability Trade-offs: Deep DAGs = long critical path. TaskGroups for organization. catchup=False to avoid backfill storms.

Cost Implications: Parallel tasks = efficient resource use. Proper dependencies = no wasted runs.

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