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.

이 연습은 강의의 일부입니다

Designing Forecasting Pipelines for Production

강의 보기

연습 안내

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

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# 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']}")

코드 편집 및 실행