automation

Airflow: Automate Workflows

Name: Airflow: Automate Workflows
Author: apache

Streamline tasks with Apache Airflow, a workflow automation platform for startups, with 46k+ GitHub stars

intermediate⏱ 30 minutes💵 Free (self-hosted)

46,249 stars17,457 forksPythonQuality 8/10Updated 7/26/2026100% free · open source

What it is

Airflow: Automate Workflows lets you create a schedule for tasks and jobs with code.

What you can make with it

Automations like: when a new customer orders on Shopify, send a confirmation email via Mailgun.

How it helps

Airflow helps you streamline complex workflows, saving time and avoiding tedious manual scheduling. It's particularly useful for large, repetitive task sets.

Real use case example

"A solo developer uses Airflow to automate a daily report on website traffic. She sets up a DAG, creates a schedule for the task to run every morning, and configures a connection to Google Analytics. Once done, the report will be sent to her email every morning at 8 am."

If you're new

Beginners will benefit from learning Airflow after they have a solid grasp of Python basics and have experimented with small scripts.

If you're senior

Senior engineers will find value in using Airflow to integrate and orchestrate workflows for large-scale data pipelines and distributed systems.

Common confusion cleared up

Airflow is often confused with Zapier, but it's primarily a workflow orchestrator, requiring code-based setup, not a simple drag-and-drop interface.

Pairs with

Google Analytics APIMailgun API

Why we list it on WorkflowStacks: This marketplace includes Airflow for its popularity and robust features as a free and open-source workflow automation platform.

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.

Install / run

pip install apache-airflow

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

Heads up: Airflow requires a database backend to store its metadata, so you need to configure a database connection, such as PostgreSQL or MySQL, before running the `airflow db init` command

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