Why scheduling automates pipeline execution in Apache Airflow - See It in Action

Why scheduling automates pipeline execution

📖 Scenario: You work in a team that builds data pipelines using Apache Airflow. Your pipelines need to run automatically every day without manual start. You want to learn how scheduling helps automate this process.

🎯 Goal: Build a simple Airflow DAG that runs a task automatically every day at 7 AM using scheduling.

📋 What You'll Learn

Create a DAG with a specific dag_id

Set the schedule_interval to run daily at 7 AM

Add a simple task using PythonOperator

Print a message when the task runs

Run the DAG and see the scheduled execution

💡 Why This Matters

🌍 Real World

Scheduling pipelines in Airflow helps teams run data workflows automatically at set times, like daily reports or hourly data updates.

💼 Career

Understanding scheduling in Airflow is key for data engineers and DevOps professionals to automate and manage workflows efficiently.

1

Create the initial DAG structure

Create a DAG called daily_pipeline with start_date set to January 1, 2024, and schedule_interval set to None (no schedule).

Apache Airflow

# Your code here

Need a hint?

Use DAG from airflow and set schedule_interval=None to disable scheduling for now.

2

Add the schedule interval for daily execution

Change the schedule_interval of the daily_pipeline DAG to run every day at 7 AM using a cron expression.

Apache Airflow

from airflow import DAG
from datetime import datetime

# Change schedule_interval to run daily at 7 AM
# Your code here

Need a hint?

Use the cron expression '0 7 * * *' to schedule the DAG daily at 7 AM.

3

Add a simple Python task to the DAG

Add a PythonOperator task called print_hello to the daily_pipeline DAG. The task should run a function that prints 'Hello, Airflow!'.

Apache Airflow

from airflow import DAG
from datetime import datetime
from airflow.operators.python import PythonOperator

# Define the function to print message
# Your code here

# Create PythonOperator task
# Your code here

Need a hint?

Define a function that prints the message. Use PythonOperator with task_id and python_callable set to your function.

4

Print the DAG and task details

Print the dag_id of the DAG and the task_id of the print_hello task to confirm setup.

Apache Airflow

from airflow import DAG
from datetime import datetime
from airflow.operators.python import PythonOperator

def print_message():
    print('Hello, Airflow!')

dag = DAG(
    dag_id='daily_pipeline',
    start_date=datetime(2024, 1, 1),
    schedule_interval='0 7 * * *'
)

print_hello = PythonOperator(
    task_id='print_hello',
    python_callable=print_message,
    dag=dag
)

# Print dag_id and task_id
# Your code here

Need a hint?

Use print(dag.dag_id) and print(print_hello.task_id) to show the IDs.