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!

Это упражнение является частью курса

Designing Forecasting Pipelines for Production

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Инструкции к упражнению

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.

Интерактивное практическое упражнение

Попробуйте выполнить это упражнение, дополнив этот пример кода.

# 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}")

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