**Why Dependencies Matter**: Without explicit order, Airflow would run all tasks at once—breaking extract-before-load semantics and exhausting resources. **Syntax**: `task_a >> task_b >> task_c` or `task_a >> [task_b, task_c]` for fan-out. **Trigger Rules**: `all_success`...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Verizon. 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.
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.
Why Dependencies Matter: Without explicit order, Airflow would run all tasks at once—breaking extract-before-load semantics and exhausting resources.
Syntax: task_a >> task_b >> task_c or task_a >> [task_b, task_c] for fan-out.
Trigger Rules: all_success (default), all_done, one_success, one_failed, none_failed, always. Use one_success for cleanup that runs when either of two branches completes.
Architectural Logic: DAG defines DAG; trigger rules handle partial failures. Deep chains (A->B->C->D->E) create long critical path; consider TaskGroups to encapsulate.
Scalability Trade-offs: Cross-DAG dependencies via ExternalTaskSensor add polling overhead. Prefer event-driven (trigger_dag) when available.
Cost Implications: Fewer wasted runs when upstream fails—downstream never starts. Use retries and retry_delay to handle transient failures.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.