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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