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適用於 Python 的 Azure Data Factory 程式庫Azure Data Factory libraries for Python

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使用 Azure Data Factory 將資料儲存、移動和處理服務撰寫到自動化的資料管線中Compose data storage, movement, and processing services into automated data pipelines with Azure Data Factory

深入了解 Data Factory，以及透過使用 Python 建立資料處理站和管線的快速入門開始第一步。Learn more about Data Factory and get started with the Create a data factory and pipeline using Python quickstart.

管理模組Management module

使用管理模組在訂用帳戶中建立和管理 Data Factory 執行個體。Create and manage Data Factory instances in your subscription with the management module.

安裝Installation

使用 pip 安裝套件：Install the package with pip:

pip install azure-mgmt-datafactory

範例Example

在美國東部地區的訂用帳戶中建立 Data Factory。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)

探索管理 APIExplore the Management APIs