Azure Data Lake Analytics libraries for python
Overview
Run big data analysis jobs that scale to massive data sets with Azure Data Lake Analytics.
Install the libraries
Management API
Use the management API to manage Data Lake Analytics accounts, jobs, policies, and catalogs.
pip install azure-mgmt-datalake-analytics
Example
This is an example of how to create a Data Lake Analytics account and submit a job.
## 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 azure.mgmt.datalake.store import DataLakeStoreAccountManagementClient
from azure.mgmt.datalake.store.models import DataLakeStoreAccount
## Required for Azure Data Lake Store filesystem management
from azure.datalake.store import core, lib, multithread
## Required for Azure Data Lake Analytics account management
from azure.mgmt.datalake.analytics.account import DataLakeAnalyticsAccountManagementClient
from azure.mgmt.datalake.analytics.account.models import DataLakeAnalyticsAccount, DataLakeStoreAccountInfo
## Required for Azure Data Lake Analytics job management
from azure.mgmt.datalake.analytics.job import DataLakeAnalyticsJobManagementClient
from azure.mgmt.datalake.analytics.job.models import JobInformation, JobState, USqlJobProperties
subid= '<Azure Subscription ID>'
rg = '<Azure Resource Group Name>'
location = '<Location>' # i.e. 'eastus2'
adls = '<Azure Data Lake Store Account Name>'
adls = '<Azure Data Lake Analytics Account Name>'
# Create the clients
resourceClient = ResourceManagementClient(credentials, subid)
adlaAcctClient = DataLakeAnalyticsAccountManagementClient(credentials, subid)
adlaJobClient = DataLakeAnalyticsJobManagementClient( credentials, 'azuredatalakeanalytics.net')
# Create resource group
armGroupResult = resourceClient.resource_groups.create_or_update(rg, ResourceGroup(location=location))
# Create a store account
adlaAcctResult = adlaAcctClient.account.create(
rg,
adla,
DataLakeAnalyticsAccount(
location=location,
default_data_lake_store_account=adls,
data_lake_store_accounts=[DataLakeStoreAccountInfo(name=adls)]
)
).wait()
# Create an ADLA account
adlaAcctResult = adlaAcctClient.account.create(
rg,
adla,
DataLakeAnalyticsAccount(
location=location,
default_data_lake_store_account=adls,
data_lake_store_accounts=[DataLakeStoreAccountInfo(name=adls)]
)
).wait()
# Submit a job
script = """
@a =
SELECT * FROM
(VALUES
("Contoso", 1500.0),
("Woodgrove", 2700.0)
) AS
D( customer, amount );
OUTPUT @a
TO "/data.csv"
USING Outputters.Csv();
"""
jobId = str(uuid.uuid4())
jobResult = adlaJobClient.job.create(
adla,
jobId,
JobInformation(
name='Sample Job',
type='USql',
properties=USqlJobProperties(script=script)
)
)
Samples
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Azure SDK for Python
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