Bibliotecas de Azure Data Factory para PythonAzure Data Factory libraries for Python
Cree servicios de almacenamiento, traslado y procesamiento de datos con canalizaciones de datos automatizadas con Azure Data FactoryCompose data storage, movement, and processing services into automated data pipelines with Azure Data Factory
Obtenga más información acerca de Data Factory y empiece a trabajar con la guía de inicio rápido Create a data factory and pipeline using Python.Learn more about Data Factory and get started with the Create a data factory and pipeline using Python quickstart.
Módulo de administraciónManagement module
Cree y administre instancias de Data Factory en su suscripción con el módulo de administración.Create and manage Data Factory instances in your subscription with the management module.
InstalaciónInstallation
Instale el paquete con pip:Install the package with pip:
pip install azure-mgmt-datafactory
EjemploExample
Cree una instancia de Data Factory en su suscripción en la región Este de EE. UU.Create a Data Factory in your subscription on the East US region.
from azure.common.credentials import ServicePrincipalCredentials
from azure.mgmt.resource import ResourceManagementClient
from azure.mgmt.datafactory import DataFactoryManagementClient
from azure.mgmt.datafactory.models import *
import time
#Create a data factory
subscription_id = '<Specify your Azure Subscription ID>'
credentials = ServicePrincipalCredentials(client_id='<Active Directory application/client ID>', secret='<client secret>', tenant='<Active Directory tenant ID>')
adf_client = DataFactoryManagementClient(credentials, subscription_id)
rg_params = {'location':'eastus'}
df_params = {'location':'eastus'}
df_resource = Factory(location='eastus')
df = adf_client.factories.create_or_update(rg_name, df_name, df_resource)
print_item(df)
while df.provisioning_state != 'Succeeded':
df = adf_client.factories.get(rg_name, df_name)
time.sleep(1)