我可以将数据从本地SQL Server数据库导入到Azure机器学习虚拟机吗？

Question

On the limited Azure Machine Learning Studio, one can import data from an On-Premises SQL Server Database. What about the ability to do the exact same thing on a python jupyter notebook on a virtual machine from the Azure Machine Learning Services workspace ?

It does not seem possible from what I've found in the documentation. Data sources would be limited in Azure ML Services : "Currently, the list of supported Azure storage services that can be registered as datastores are Azure Blob Container, Azure File Share, Azure Data Lake, Azure Data Lake Gen2, Azure SQL Database, Azure PostgreSQL, and Databricks File System"

Thank you in advance for your assistance

Answer 1

As of today, you can load SQL data, but only a MS SQL Server source (also on-premise) is supported .

Using azureml.dataprep , code would read along the lines of

import azureml.dataprep as dprep

secret = dprep.register_secret(value="[SECRET-PASSWORD]", id="[SECRET-ID]")

ds = dprep.MSSQLDataSource(server_name="[SERVER-NAME]",
                           database_name="[DATABASE-NAME]",
                           user_name="[DATABASE-USERNAME]",
                           password=secret)

dflow = dprep.read_sql(ds, "SELECT top 100 * FROM [YourDB].[ATable]")
# print first records
dflow.head(5)

As far as I understand the APIs are under heavy development and azureml.dataprep may be soon superseded by functionality provided by the Dataset class .

Answer 2

You can always push the data to a supported source using a data movement/orchestration service. Remember that all Azure services are not going to have every source option like Power BI, Logic Apps or Data Factory...this is why data orchestration/movement services exist.