Azure Data Lake Analytics Management Client Library for Python

0.6.0 · maintenance · verified Sat Apr 11

This is the Microsoft Azure Data Lake Analytics Management Client Library for Python, version 0.6.0. It provides programmatic access to manage Azure Data Lake Analytics accounts, jobs, policies, and catalogs through Azure Resource Manager (ARM). The 0.6.0 version was released in 2018 and was tested with Python 2.7, 3.4, 3.5, and 3.6. While a beta version 1.0.0b2 was released in late 2022, the 0.6.0 stable version represents an older generation of Azure SDKs, likely in maintenance mode, with a less frequent release cadence compared to newer 'Track 2' SDKs.

Warnings

deprecated The `azure-mgmt-datalake-analytics` library, particularly version 0.6.0, is an older 'Track 1' Azure SDK. While Azure Data Lake Analytics is still a service, newer Python SDKs for Azure services typically follow 'Track 2' guidelines (e.g., `azure-storage-file-datalake` for ADLS Gen2). For new development or managing Data Lake Storage, consider modern alternatives.
Fix: Evaluate if Azure Data Lake Analytics is the appropriate service for your needs. For managing Data Lake Storage Gen2, consider `azure-storage-file-datalake` (Track 2 SDK). For general Azure resource management, use the corresponding Track 2 SDKs for other services.
breaking Versions 0.5.0 and 0.6.0 introduced significant breaking changes, primarily due to a 'next-generation code generator'. This affected model signatures, which now primarily use keyword-argument syntax, and Long Running Operation (LRO) return types.
Fix: Review release notes for 0.5.0 and 0.6.0. Update code to use keyword arguments for model instantiation and client methods. Adjust LRO polling logic if migrating from versions older than 0.5.0.
gotcha Older Azure Python SDKs (prior to 1.0) and some samples used `msrestazure.azure_active_directory.AADTokenCredentials` or `adal` for authentication. The recommended and more robust approach for modern Azure SDKs is `azure.identity.DefaultAzureCredential`.
Fix: Use `DefaultAzureCredential` from `azure-identity` for authentication. This simplifies credential management by attempting various authentication flows (environment variables, managed identity, etc.). Ensure `azure-identity` is installed: `pip install azure-identity`.
gotcha The 0.6.0 version officially supports Python 2.7, 3.4, 3.5, and 3.6. Using it with newer Python versions (e.g., Python 3.7+) may lead to unexpected behavior or compatibility issues, although a beta 1.0.0b2 exists for Python 3.7+.
Fix: For Python 3.7+, consider trying the `1.0.0b2` beta version, or ideally, explore modern Azure SDKs for relevant Data Lake services. If you must use 0.6.0, ensure your environment uses a supported Python version.

Install

pip install azure-mgmt-datalake-analytics Install stable version
pip install azure-mgmt-datalake-analytics==1.0.0b2 Install latest beta version (Python 3.7+)

Imports

DataLakeAnalyticsAccountManagementClient

from azure.mgmt.datalake.analytics import DataLakeAnalyticsAccountManagementClient

DefaultAzureCredential
```
from azure.identity import DefaultAzureCredential
```
DefaultAzureCredential is the recommended modern way for authentication. AADTokenCredentials was used in older SDKs and samples for specific scenarios like device code flow.

Quickstart

This quickstart demonstrates how to authenticate with Azure using `DefaultAzureCredential` and then list existing Azure Data Lake Analytics accounts. Ensure your Azure credentials (e.g., Tenant ID, Client ID, Client Secret, Subscription ID) are configured as environment variables or other methods supported by `DefaultAzureCredential`.

import os
from azure.identity import DefaultAzureCredential
from azure.mgmt.datalake.analytics.account import DataLakeAnalyticsAccountManagementClient

# Set environment variables for authentication (e.g., AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, AZURE_SUBSCRIPTION_ID)
# For a quick start, ensure these are set or use Managed Identity in Azure environments.
# Example: export AZURE_TENANT_ID="<your-tenant-id>"
#          export AZURE_CLIENT_ID="<your-client-id>"
#          export AZURE_CLIENT_SECRET="<your-client-secret>"
#          export AZURE_SUBSCRIPTION_ID="<your-subscription-id>"

subscription_id = os.environ.get('AZURE_SUBSCRIPTION_ID', 'YOUR_SUBSCRIPTION_ID')

# Authenticate using DefaultAzureCredential (recommended for modern Azure SDK usage)
# This credential chain tries various methods including environment variables, managed identity, etc.
credential = DefaultAzureCredential()

# Create the Data Lake Analytics Management Client
client = DataLakeAnalyticsAccountManagementClient(credential, subscription_id)

# Example: List Data Lake Analytics accounts in a subscription
print("Listing Data Lake Analytics accounts:")
for account in client.account.list():
    print(f"  - {account.name} (Location: {account.location})")

print("Quickstart finished successfully (if no errors occurred and accounts were listed).")

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