BigData / Apache Airflow Interview Questions

What is the difference between Airflow and Apache Spark?

Airflow and Spark serve different purposes and are frequently used together:

Aspect	Apache Airflow	Apache Spark
Purpose	Workflow orchestration — define, schedule, and monitor pipelines	Distributed data processing — transform and analyze large datasets in memory
Data	Does not process data itself; delegates to operators/hooks	Processes terabytes of data in parallel across a cluster
Language	Python (DAGs)	Scala, Python (PySpark), Java, R
Execution	Task scheduling on workers	In-memory RDD/DataFrame transformations on executors

A common pattern: Airflow submits a Spark job via SparkSubmitOperator or LivyOperator, then monitors its completion.

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What is the primary role of Apache Airflow compared to Apache Spark? Airflow orchestrates workflows; Spark processes data

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Airflow processes data; Spark schedules jobs

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They are interchangeable tools

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