Apache Airflow Tutorial : Create Your First Dag Step By Step - 2026
#apacheairflow #airflow #airflowdag #airflowtutorial #firstdag #learnairflow
Let's create your first Apache Airflow DAG! Below is a step-by-step guide and a simple example to help you get started.
Step 1: Prerequisites
Install Apache Airflow (if not already installed):
bash
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pip install apache-airflow
Initialize the Airflow Environment:
bash
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airflow db init
airflow users create \
--username admin \
--firstname Airflow \
--lastname Admin \
--role Admin \
--email [email protected]
Start the Airflow Web Server and Scheduler:
bash
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airflow webserver --port 8080
airflow scheduler
Access the web interface at http://localhost:8080.
Step 2: Create Your First DAG
A DAG (Directed Acyclic Graph) defines a workflow. Tasks are defined in Python and orchestrated by Airflow.
Example: My First DAG
python
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from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime
Define the DAG
default_args = {
'owner': 'airflow',
'retries': 1,
'retry_delay': timedelta(minutes=5)
}
with DAG(
dag_id='my_first_dag',
default_args=default_args,
description='My first Airflow DAG!',
schedule_interval='@daily',
start_date=datetime(2023, 1, 1),
catchup=False,
) as dag:
Task 1: Print the current date
print_date = BashOperator(
task_id='print_date',
bash_command='date'
)
Task 2: Say Hello
say_hello = BashOperator(
task_id='say_hello',
bash_command='echo "Hello, Airflow!"'
)
Task Dependencies
print_date say_hello
Step 3: Add the DAG to Airflow
Save the file as my_first_dag.py in the Airflow dags/ directory:
bash
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cp my_first_dag.py $AIRFLOW_HOME/dags/
Airflow will automatically detect the DAG and display it in the web interface under "DAGs."
Step 4: Run the DAG
In the Airflow UI, activate the DAG by toggling the "ON" switch.
Trigger the DAG manually using the play button or wait for the scheduled interval.
Explanation of the DAG
DAG Definition:
dag_id: Unique identifier for the DAG.
start_date: The date the DAG starts running.
schedule_interval: Determines how often the DAG runs (@daily, @hourly, */5 * * * *, etc.).
catchup: Ensures Airflow doesn’t backfill runs for past dates if set to False.
Tasks:
BashOperator: Executes a bash command.
print_date: Prints the current date.
say_hello: Prints "Hello, Airflow!" to the logs.
Task Dependencies:
print_date say_hello: Ensures that say_hello runs after print_date.
Would you like help with more advanced DAG features like using PythonOperators, XComs, or dynamic task generation?