Airflow Tutorial For Beginners (2026) | Apache Airflow Full Course

Introduction

This tutorial provides a comprehensive guide to Apache Airflow, aimed at beginners looking to learn the fundamentals and gain practical skills for becoming an Airflow Developer or Data Engineer. Apache Airflow is a powerful tool for orchestrating complex workflows, and this guide will walk you through its core concepts, installation, and usage.

Step 1: Understanding Apache Airflow

• Airflow is a platform to programmatically author, schedule, and monitor workflows.
• It allows users to define workflows as Directed Acyclic Graphs (DAGs).
• Airflow is not a data processing tool; it's designed for automation and orchestration.

Step 2: Key Components of Airflow

• DAG (Directed Acyclic Graph): A collection of tasks with defined dependencies.
• Tasks: The individual units of work within a DAG.
• Operators: Define what task is going to be executed, such as BashOperator, PythonOperator, etc.

Step 3: Setting Up Airflow

• Install Airflow using the following command:

  pip install apache-airflow
  

• Initialize the database with:

  airflow db init
  

• Start the web server:

  airflow webserver --port 8080
  

• Start the scheduler:

  airflow scheduler
  

Step 4: Exploring the Airflow User Interface

• Access the Airflow UI at http://localhost:8080.
• Familiarize yourself with the dashboard, where you can view DAGs, task instances, and logs.

Step 5: Creating a Simple DAG

1. Create a new Python file in the DAGs directory.
2. Define your DAG and tasks as follows:

   from airflow import DAG
   from airflow.operators.dummy_operator import DummyOperator
   from datetime import datetime
   
   default_args = {
       'owner': 'airflow',
       'start_date': datetime(2023, 1, 1),
   }
   
   dag = DAG('simple_dag', default_args=default_args, schedule_interval='@daily')
   
   start_task = DummyOperator(task_id='start', dag=dag)
   end_task = DummyOperator(task_id='end', dag=dag)
   
   start_task >> end_task
   

3. Save the file, and the DAG should appear in the Airflow UI.

Step 6: Advanced Concepts

• Versioning DAGs: Keep track of changes in your DAGs to prevent issues during deployment.
• XComs: Used for sharing information between tasks. Use the following command to push and pull XCom values:

  ti.xcom_push(key='my_key', value='my_value')
  value = ti.xcom_pull(key='my_key')
  

• Parallel Tasks: Execute multiple tasks simultaneously to optimize performance.

Step 7: Scheduling and Triggers

• Learn about scheduling presets and cron syntax to control when your DAGs run.
• Explore incremental loads and Jinja templates for dynamic task configurations.

Conclusion

This tutorial has introduced you to the basics of Apache Airflow, including its architecture, components, and practical setup. By following these steps, you can start creating and managing workflows effectively. For further learning, consider exploring advanced topics such as DAG versioning and XComs. With practice, you'll enhance your skills as an Airflow Developer.