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Airflow

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description Airflow Overview

Apache Airflow is the leading platform for programmatically defining, scheduling, and monitoring workflows. It uses Directed Acyclic Graphs (DAGs) to manage complex sequences of tasks, making it a staple in data engineering for ETL pipelines. While not designed for real-time microservice management, its ability to handle massive amounts of data processing and integrate with hundreds of external services makes it indispensable for backend data operations.

help Airflow FAQ

What is an Airflow DAG?

In Apache Airflow, a DAG is a Directed Acyclic Graph that defines tasks and their dependencies. Each DAG describes what runs first, what waits, what can run in parallel, and how failures are handled.

Is Airflow meant for real-time streaming data?

Airflow is mainly for scheduled and orchestrated batch workflows, not low-latency streaming. For real-time event processing, teams more often use systems such as Kafka, Flink, or Spark Structured Streaming.

Why do data teams use Airflow for ETL pipelines?

Airflow lets teams define workflows in Python, schedule them, retry failed tasks, and monitor runs through a web UI. It is commonly used to coordinate jobs across warehouses, object storage, dbt, Spark, APIs, and internal scripts.

Who originally created Apache Airflow?

Airflow was created at Airbnb before becoming an Apache Software Foundation project. That origin explains why it was built around managing many scheduled data workflows in a production data platform.

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