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.

Este exercício faz parte do curso

Designing Forecasting Pipelines for Production

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Instruções do exercício

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

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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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