Apache Airflow
Also known as: Airflow
Apache Airflow is an open-source platform for scheduling, running, and monitoring workflows, especially data pipelines.
Workflows are written in Python as DAGs (directed acyclic graphs), which describe tasks and the order they run in. While newer tools have challenged it, Airflow remains one of the most widely used orchestrators, largely because so many teams already know it and so many integrations exist.
Apache Airflow, explained at three levels
At home
Imagine a to-do list where some tasks must wait for others: you can't bake the cake before buying flour. Airflow manages a list like that for computers, starting each task at the right time and in the right order.
At work
Airflow automates routine data jobs, like loading sales data overnight, cleaning it, and refreshing dashboards. It shows which steps succeeded or failed, so teams can fix problems quickly.
At home
In Airflow, a workflow is a DAG: tasks connected by arrows showing dependencies, with no loops. Each task might run a SQL query, call an API, or start a script. A web interface shows runs, timings, and errors.
At work
Airflow offers thousands of integrations through provider packages, retries and alerting, backfills for past dates, and scheduling based on time or data availability. Teams often run it on Kubernetes or use a managed service to avoid maintaining the scheduler and workers themselves.
At home
Airflow is designed to orchestrate work, not do the heavy lifting. In my view, the most common beginner mistake is processing large data inside Airflow tasks, which overwhelms workers, instead of telling a warehouse or Spark cluster to do it.
At work
Operational issues include scheduler performance with many DAGs, dependency conflicts between Python packages, and testing DAG code. Consider data-aware alternatives such as Dagster, or event-driven tools, when lineage and asset tracking matter more than time-based schedules. Keep DAGs thin and push logic into versioned, testable code.
Real-world examples
At home
Thomas built a small project to track Canadian gas prices. He used Airflow to scrape prices every morning, clean them, and update a chart, and its interface showed him exactly which day the scraper broke.
At work
Carlos is a data engineer at a Winnipeg utility. Their overnight billing pipeline had 40 steps run by cron jobs nobody understood. Moving it to Airflow made dependencies visible, and failures now alert the on-call engineer with the exact failed step.
How it compares
| feature | Apache Airflow | Cron jobs |
|---|---|---|
| Workflow definition | Python DAGs | Time-based schedule lines |
| Dependencies | Built in | Not supported |
| Monitoring | Web interface, retries, alerts | Manual log checks |
| Setup effort | Significant | Minimal |
Cron runs a task on time. Airflow runs a whole workflow correctly.
For analysts
When reports are late, the cause is often an invisible chain of scheduled jobs. I'd map those dependencies before choosing a tool, because the map alone often reveals steps nobody needs anymore.
History & origin
Maxime Beauchemin created Airflow at Airbnb in 2014 to manage the company's growing data workflows, and Airbnb open-sourced it in 2015. It entered the Apache incubator in 2016 and became a top-level Apache project in January 2019. Managed versions followed from Google (Cloud Composer, 2018), AWS (Managed Workflows for Apache Airflow, 2020), and Astronomer. Airflow 2.0 in 2020 improved scheduler performance, and Airflow 3.0, released in April 2025, added DAG versioning and a redesigned interface. Competitors such as Dagster and Prefect argued for data-aware orchestration. In my opinion, Airflow is the Excel of data engineering: everyone has complaints, and almost everyone still uses it.