Manage Azure Data Lake Analytics using Python

This article describes how to manage Azure Data Lake Analytics accounts, data sources, users, and jobs by using Python.

Supported Python versions

  • Use a 64-bit version of Python.
  • You can use the standard Python distribution found at downloads.
  • Many developers find it convenient to use the Anaconda Python distribution.
  • This article was written using Python version 3.6 from the standard Python distribution

Install Azure Python SDK

Install the following modules:

  • The azure-mgmt-resource module includes other Azure modules for Active Directory, etc.
  • The azure-datalake-store module includes the Azure Data Lake Store filesystem operations.
  • The azure-mgmt-datalake-store module includes the Azure Data Lake Store account management operations.
  • The azure-mgmt-datalake-analytics module includes the Azure Data Lake Analytics operations.

First, ensure you have the latest pip by running the following command:

python -m pip install --upgrade pip

This document was written using pip version 9.0.1.

Use the following pip commands to install the modules from the commandline:

pip install azure-mgmt-resource
pip install azure-datalake-store
pip install azure-mgmt-datalake-store
pip install azure-mgmt-datalake-analytics

Create a new Python script

Paste the following code into the script:

## Use this only for Azure AD service-to-service authentication
#from azure.common.credentials import ServicePrincipalCredentials

## Use this only for Azure AD end-user authentication
#from azure.common.credentials import UserPassCredentials

## Required for Azure Resource Manager
from azure.mgmt.resource.resources import ResourceManagementClient
from azure.mgmt.resource.resources.models import ResourceGroup

## Required for Azure Data Lake Store account management
from import DataLakeStoreAccountManagementClient
from import DataLakeStoreAccount

## Required for Azure Data Lake Store filesystem management
from import core, lib, multithread

## Required for Azure Data Lake Analytics account management
from import DataLakeAnalyticsAccountManagementClient
from import DataLakeAnalyticsAccount, DataLakeStoreAccountInformation

## Required for Azure Data Lake Analytics job management
from import DataLakeAnalyticsJobManagementClient
from import JobInformation, JobState, USqlJobProperties

## Required for Azure Data Lake Analytics catalog management
from import DataLakeAnalyticsCatalogManagementClient

## Use these as needed for your application
import logging, getpass, pprint, uuid, time

Run this script to verify that the modules can be imported.


Interactive user authentication with a pop-up

This method is not supported.

Interactive user authentication with a device code

user = input('Enter the user to authenticate with that has permission to subscription: ')
password = getpass.getpass()
credentials = UserPassCredentials(user, password)

Noninteractive authentication with SPI and a secret

credentials = ServicePrincipalCredentials(client_id = 'FILL-IN-HERE', secret = 'FILL-IN-HERE', tenant = 'FILL-IN-HERE')

Noninteractive authentication with API and a certificate

This method is not supported.

Common script variables

These variables are used in the samples.

subid= '<Azure Subscription ID>'
rg = '<Azure Resource Group Name>'
location = '<Location>' # i.e. 'eastus2'
adls = '<Azure Data Lake Store Account Name>'
adla = '<Azure Data Lake Analytics Account Name>'

Create the clients

resourceClient = ResourceManagementClient(credentials, subid)
adlaAcctClient = DataLakeAnalyticsAccountManagementClient(credentials, subid)
adlaJobClient = DataLakeAnalyticsJobManagementClient( credentials, '')

Create an Azure Resource Group

armGroupResult = resourceClient.resource_groups.create_or_update( rg, ResourceGroup( location=location ) )

Create Data Lake Analytics account

First create a store account.

adlsAcctResult = adlsAcctClient.account.create(

Then create an ADLA account that uses that store.

adlaAcctResult = adlaAcctClient.account.create(

Submit a job

script = """
@a  = 
            ("Contoso", 1500.0),
            ("Woodgrove", 2700.0)
        ) AS 
              D( customer, amount );
    TO "/data.csv"
    USING Outputters.Csv();

jobId = str(uuid.uuid4())
jobResult = adlaJobClient.job.create(
		name='Sample Job',

Wait for a job to end

jobResult = adlaJobClient.job.get(adla, jobId)
while(jobResult.state != JobState.ended):
	print('Job is not yet done, waiting for 3 seconds. Current state: ' + jobResult.state.value)
	jobResult = adlaJobClient.job.get(adla, jobId)

print ('Job finished with result: ' + jobResult.result.value)

List pipelines and recurrences

Depending whether your jobs have pipeline or recurrence metadata attached, you can list pipelines and recurrences.

pipelines = adlaJobClient.pipeline.list(adla)
for p in pipelines:
	print('Pipeline: ' + + ' ' + p.pipelineId)

recurrences = adlaJobClient.recurrence.list(adla)
for r in recurrences:
	print('Recurrence: ' + + ' ' + r.recurrenceId)

Manage compute policies

The DataLakeAnalyticsAccountManagementClient object provides methods for managing the compute policies for a Data Lake Analytics account.

List compute policies

The following code retrieves a list of compute policies for a Data Lake Analytics account.

policies = adlaAccountClient.computePolicies.listByAccount(rg, adla)
for p in policies:
	print('Name: ' + + 'Type: ' + p.objectType + 'Max AUs / job: ' + p.maxDegreeOfParallelismPerJob + 'Min priority / job: ' + p.minPriorityPerJob)

Create a new compute policy

The following code creates a new compute policy for a Data Lake Analytics account, setting the maximum AUs available to the specified user to 50, and the minimum job priority to 250.

userAadObjectId = "3b097601-4912-4d41-b9d2-78672fc2acde"
newPolicyParams = ComputePolicyCreateOrUpdateParameters(userAadObjectId, "User", 50, 250)
adlaAccountClient.computePolicies.createOrUpdate(rg, adla, "GaryMcDaniel", newPolicyParams)

Next steps