Apache Airflow: Automate Complex Data Workflows Like a Pro
Professional workflow orchestration for data engineers and founders building scalable data pipelines across ML, analytics, and integration projects.
45,667 stars17,152 forksPythonUpdated 6/2/2026100% free · open source
What it does
Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows to build scalable data pipelines across ML, analytics, and integration projects.
When to use it
•When you need to manage complex data workflows across multiple systems and teams
•To automate and schedule recurring data tasks, such as data ingestion and processing
•For building and managing machine learning pipelines that require data preparation, training, and deployment
Quick start
1Create a new Airflow project by running `airflow db init` to initialize the database
2Define a DAG (Direct Acyclic Graph) in a Python file, e.g., `my_dag.py`, using the `DefaultArgs` and `DAG` classes from the Airflow library
3Configure the Airflow scheduler to run your DAG by setting the `start_date` and `schedule_interval` parameters
4Run the Airflow web server with `airflow webserver --port 8080` to monitor and manage your workflows
5Trigger a DAG run manually with `airflow trigger_dag my_dag` or schedule it to run automatically
Ready-to-paste prompt
Run a sample DAG with `airflow trigger_dag example_bash_operator` to see how Airflow can execute a series of bash commands
Topics
airflow
apache
apache-airflow
automation
dag
data-engineering
data-integration
data-orchestrator
data-pipelines
data-science
elt
etl
machine-learning
mlops
orchestration
python
scheduler
workflow
workflow-engine
workflow-orchestration
Quick Actions
Details
Creator
apache
Language
Python
Category
automation
Published
4/13/2015
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