Azure Automation

It's surprising how many silly little jobs can be automated away for big productivity wins. I wrote some Python code to run on a laptop that plays with Azure cloud. The main takeaway points were:

The best way to get access is by using azure.identity.InteractiveBrowserCredential in the azure-identity package. This opens the browser and prompts the user to login. This can then be used to instantiate a azure.storage.filedatalake.DataLakeServiceClient in azure-storage-file-datalake. With this we can upload files.

Then, to kick off ADF jobs, we can use the same credential and pass it to azure.mgmt.datafactory.DataFactoryManagementClient in azure-mgmt-datafactory. As a nice little aside, you can use webbrowser in the web-browser package that will open your browser to the ADF job by crafting a URL with the job ID so you can monitor its progress.

This all seemed pretty straight forward but accessing an Azure SQL instance was somewhat harder. To access the credential, I referred to this SO answer. Here I used the InteractiveBrowserCredential as before but then I needed:

    token = credential.get_token("https://database.windows.net/.default")

    connection_string = f"""

        Driver={Driver};

        Server={server};

        Database={database};

        Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;

    """

    token_bytes = token.token.encode("UTF-16-LE")

    token_struct = struct.pack(f'<I{len(token_bytes)}s', len(token_bytes), token_bytes)

    conn = pyodbc.connect(connection_string, attrs_before = { 1256:token_struct });

which is a bit more involved but allowed me to connect.