Apache Airflow Setup with Docker + PythonOperator DAG Explained | Step-by-Step Beginner Guide
Welcome back to SummarizedAI
In this video, we will learn how to set up Apache Airflow locally using Docker and build your first PythonOperator DAG step by step.
If you are starting with Data Engineering, Workflow Automation, or ETL pipelines, this is one of the most important hands-on tutorials to get started with Airflow.
What you will learn in this video:
1. How to install WSL on Windows
2. How to check WSL version
3. How to build a custom Apache Airflow Docker image
4. Understanding Dockerfile step-by-step
5. Creating docker-compose for Airflow setup
6. Running Airflow in standalone mode
7. Writing your first DAG using PythonOperator
8. Executing Python functions as Airflow tasks
WSL Setup Commands:
wsl --install
wsl --version
Dockerfile Explanation
We start from an existing Airflow image:
FROM apache/airflow:2.8.4
What happens next:
1. Switch to root user
2. Install required packages like Git
3. Clean cache to reduce image size
4. Switch back to airflow user for security
USER root
RUN apt-get update && \
apt-get install -y git && \
apt-get clean
USER airflow
Why this is important:
Running Airflow as root is unsafe, so we switch back to a non-root user.
Build Docker Image
docker build -t test-airflow:latest .
docker-compose.yaml Explanation
We define how Airflow runs as a container:
services:
airflow:
image: test-airflow:latest
volumes:
./airflow:/opt/airflow
ports:
"8080:8080"
command: airflow standalone
Key points:
services → defines containers
image → Docker image used
volumes → sync local folder with container
ports → maps container port to local machine
command → runs Airflow in standalone mode
Python DAG Code (PythonOperator Example)
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
def hello_dag():
print("Hello DAG")
with DAG(
dag_id="test_sample_dag",
start_date=datetime(2026,5,23),
schedule="@daily",
catchup=False
) as dag:
task1 = PythonOperator(
task_id="hello_dag",
python_callable=hello_dag
)
task1
How it works:
1. DAG defines the workflow
2. PythonOperator executes Python function
3. Task runs inside Airflow scheduler
4. Output is logged in Airflow UI
Summary:
By the end of this video, you will understand:
1. How to set up Airflow using Docker
2. How PythonOperator executes Python functions
3. How DAGs are structured in real-world workflows
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