How to Build an ETL App for Power BI XMLA Data in Python with CData

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

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

Connecting to Power BI XMLA Data

Connecting to Power BI XMLA 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.

By default, use Azure AD to connect to Microsoft Power BI XMLA. Azure AD is Microsoft’s multi-tenant, cloud-based directory and identity management service. It is user-based authentication that requires that you set AuthScheme to AzureAD.

For more information on other authentication schemes, refer to the Help documentation.

After installing the CData Power BI XMLA Connector, follow the procedure below to install the other required modules and start accessing Power BI XMLA 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 Power BI XMLA 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.powerbixmla as mod

You can now connect with a connection string. Use the connect function for the CData Power BI XMLA Connector to create a connection for working with Power BI XMLA data.

cnxn = mod.connect("AuthScheme=AzureADInitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Power BI XMLA

Use SQL to create a statement for querying Power BI XMLA. In this article, we read data from the Customer entity.

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

Extract, Transform, and Load the Power BI XMLA Data

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

Loading Power BI XMLA 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 Power BI XMLA, you can work with Power BI XMLA 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 CData Python Connector for Power BI XMLA to start building Python apps and scripts with connectivity to Power BI XMLA 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.powerbixmla as mod

cnxn = mod.connect("AuthScheme=AzureADInitiateOAuth=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')