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.

この演習はコースの一部です

Designing Forecasting Pipelines for Production

コースを見る

演習の手順

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

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# 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']}")

コードを編集して実行