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
Think of a to-do list where some tasks must wait for others: you can't ice the cake before baking it, or bake it before buying flour. Someone has to start each task at the right time and notice when one fails. Apache Airflow is that organizer for computer jobs, such as nightly data loads, running each step in order and tracking what succeeded.
At work
Apache Airflow is open-source software for scheduling, running, and monitoring workflows, especially data pipelines. Teams define each workflow as Python code that lists tasks, such as loading sales data, cleaning it, and refreshing dashboards, and the order they run in. Businesses care because Airflow replaces fragile chains of scheduled scripts with visible, retryable workflows, so failures are spotted and fixed before reports go out wrong.
At home
In Airflow, a workflow is a DAG, a directed acyclic graph: tasks joined by arrows showing which must finish first, with no loops back. Each task uses an operator, a ready-made building block that runs a SQL query, calls an API, or starts a script. A scheduler decides when each DAG should run and which tasks are ready, and a web interface shows every run, its timing, and any errors.
At work
Airflow has several components. The scheduler reads DAG files, decides which tasks are ready, and hands them to an executor, which runs them on workers, such as Celery workers or Kubernetes pods. A metadata database records every run and its state, and the web server displays it. Provider packages add thousands of integrations with clouds and databases. Features include retries, alerting, backfills for past dates, and data-aware scheduling that triggers a DAG when upstream data updates.
At home
Airflow is an orchestrator: it tells other systems when to work, but it isn't built to do heavy lifting itself. Its workers are general-purpose machines, and passing large data between tasks through Airflow's own storage is slow and fragile. While it's tempting to load a big file into a Python task, in my view the right pattern is telling a warehouse or Spark cluster to do the processing and having Airflow simply track it.
At work
Airflow's scheduler parses every DAG file repeatedly, so heavy top-level code and thousands of DAGs slow it down. Python dependency conflicts between DAGs are common, which is one reason Airflow 3 added a task execution interface that lets tasks run in isolated or remote environments, alongside DAG versioning. Test DAG structure in CI and keep business logic in versioned packages. Consider asset-oriented alternatives such as Dagster where lineage and data freshness matter more than time-based schedules.
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.