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Apache Airflow is a platform to program, schedule and monitor batch workflows and data pipelines. Tasks are defined as Python DAGs and executed, monitored and retried centrally. Airflow is used for ETL, data integration and recurring automation across distributed environments. It supports scaling and integrations with common data platforms.
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Apache Airflow is a platform for defining, scheduling, and monitoring batch workflows and data pipelines as Python DAGs.
Apache Airflow originated at Airbnb in 2014, when Maxime Beauchemin built a solution for the company’s growing workflow complexity. From the beginning, workflows were described in Python, scheduled centrally, and monitored through a user interface. The project entered the Apache Incubator in 2016 and became an Apache Software Foundation Top-Level Project in 2019.
Think of Airflow as an operations desk with three layers: Python code defines the DAG as a network of tasks, the scheduler turns it into concrete runs, and execution returns status, logs, and failures. If a step breaks, Airflow can retry it under defined rules. That keeps order, dependencies, and operational visibility connected but distinct.
A directed acyclic graph fixes dependencies and execution order.
The scheduler starts runs on a time basis or in response to triggers.
A task is the smallest executable unit with its own logic and state.
Failures can be run again for a defined number of attempts.
Coordinates data flows, processing steps, and dependencies across multiple systems.
Add-on packages connect Airflow to external services and platforms.
Apache Airflow is useful when workflows are scheduled, span multiple systems, and need visible failure handling. Typical cases include ETL, load and validation jobs, and recurring automation. The trade-off is that you own the workflow code, operations, and dependencies; Airflow coordinates the process but does not replace the processing engine or data quality controls.
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