Librerie di Azure Data Factory per PythonAzure Data Factory libraries for Python
Azure Data Factory consente di comporre servizi di archiviazione, spostamento ed elaborazione di dati in pipeline di dati automatizzate.Compose data storage, movement, and processing services into automated data pipelines with Azure Data Factory
Vedere altre informazioni su Data Factory e iniziare con la guida introduttiva Creare una data factory e una pipeline con Python.Learn more about Data Factory and get started with the Create a data factory and pipeline using Python quickstart.
Modulo di gestioneManagement module
Creare e gestire istanze di Data Factory nella sottoscrizione con il modulo di gestione.Create and manage Data Factory instances in your subscription with the management module.
InstallazioneInstallation
Installare il pacchetto usando pip:Install the package with pip:
pip install azure-mgmt-datafactory
EsempioExample
Creare una data factory nella sottoscrizione nell'area Stati Uniti orientali.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)