Setting up the DAG

Orchestration tools such as Apache Airflow are essential for automating data and machine learning workflows.

In this exercise, you'll begin setting up a Directed Acyclic Graph (DAG) by importing the required classes and configuring default arguments that define how your pipeline will run.

This exercise is part of the course

Designing Forecasting Pipelines for Production

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Exercise instructions

Import the DAG and PythonOperator classes from Airflow.
Set the start date as 7th July, 2025.
Set email_on_failure to False.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import required classes
from airflow import ____
from airflow.providers.standard.operators.python import ____
from datetime import datetime

default_args = {
  'owner': 'airflow',
  # Define the arguments
  'depends_on_past': False,
  'start_date': datetime(____),
  'email_on_failure': ____}

print(f"DAG configured to start on {default_args['start_date']}")

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