Creating the DAG

After setting the default arguments, it's time to define your DAG and create the first task that checks the API. This step is essential for automating your data and machine learning workflows. The following modules were imported: DAG, PythonOperator, and datetime. You also have a custom check_updates_api function available. Time to build your DAG!

이 연습은 강의의 일부입니다

Designing Forecasting Pipelines for Production

강의 보기

연습 안내

Define the DAG using the right function.
Set the schedule to run daily.
Create the check_api task using a Python operator.
Provide the check_updates_api function as the callable.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Define the DAG
with ____(
    'data_pipeline',
    default_args=default_args,
    description='Data pipeline for ETL process',
  	# Set the schedule to run daily
    schedule='@____',
    tags = ["python", "etl", "forecast"]
) as dag:
  # Create check_api
  check_api = ____(
    task_id='check_api',
    # Use the check_updates_api function
    python_callable=____)

print(f"DAG object created: {dag}")
print(f"PythonOperator for API check created: {check_api}")

코드 편집 및 실행