Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Schedule tasks in Airflow: (1) Define DAG with schedule_interval (cron or timedelta); (2) Add tasks; (3) Set dependencies: task1 >>...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like BCG. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow, optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Schedule tasks in Airflow: (1) Define DAG with schedule_interval (cron or timedelta); (2) Add tasks; (3) Set dependencies: task1 >> task2; (4) Enable DAG. Example: from airflow import DAG; from airflow.operators.python import PythonOperator; dag = DAG('my_dag', schedule_interval='0 2 *'); task = PythonOperator(task_id='run', python_callable=my_func, dag=dag). Trigger: manually via UI/CLI or by schedule. Best practices: use idempotent tasks; set retries; use Variables for env-specific config; monitor DAG runs; avoid long-running tasks in same process as scheduler.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: catchup=True without understanding. Pro-Move: 'Idempotent tasks; retry; Variables for config.'
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.