Extract, Transform, and Load Azure Analysis Services Data in Python

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Azure Analysis Services Python Connector

Python Connector Libraries for Azure Analysis Services Data Connectivity. Integrate Azure Analysis Services with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Azure Analysis Services enables you to create ETL applications and pipelines for Azure Analysis Services data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Azure Analysis Services and the petl framework, you can build Azure Analysis Services-connected applications and pipelines for extracting, transforming, and loading Azure Analysis Services data. This article shows how to connect to Azure Analysis Services with the CData Python Connector and use petl and pandas to extract, transform, and load Azure Analysis Services data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Azure Analysis Services data in Python. When you issue complex SQL queries from Azure Analysis Services, the driver pushes supported SQL operations, like filters and aggregations, directly to Azure Analysis Services and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Azure Analysis Services Data

Connecting to Azure Analysis Services data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to Azure Analysis Services, set the Url property to a valid server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating. Optionally, set Database to distinguish which Azure database on the server to connect to.

Azure Analysis Services uses the OAuth authentication standard. OAuth requires the authenticating user to interact with Azure Analysis Services using the browser. You can connect without setting any connection properties for your user credentials. See the Help documentation for more information.

After installing the CData Azure Analysis Services Connector, follow the procedure below to install the other required modules and start accessing Azure Analysis Services through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Azure Analysis Services Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.aas as mod

You can now connect with a connection string. Use the connect function for the CData Azure Analysis Services Connector to create a connection for working with Azure Analysis Services data.

cnxn = mod.connect("URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Azure Analysis Services

Use SQL to create a statement for querying Azure Analysis Services. In this article, we read data from the Customer entity.

sql = "SELECT Country, Education FROM Customer WHERE Country = 'Australia'"

Extract, Transform, and Load the Azure Analysis Services Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Azure Analysis Services data. In this example, we extract Azure Analysis Services data, sort the data by the Education column, and load the data into a CSV file.

Loading Azure Analysis Services Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Education')

etl.tocsv(table2,'customer_data.csv')

With the CData Python Connector for Azure Analysis Services, you can work with Azure Analysis Services data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Azure Analysis Services Python Connector to start building Python apps and scripts with connectivity to Azure Analysis Services data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.aas as mod

cnxn = mod.connect("URL=asazure://REGION.asazure.windows.net/server;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Country, Education FROM Customer WHERE Country = 'Australia'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Education')

etl.tocsv(table2,'customer_data.csv')