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Python Connector Libraries for Power BI XMLA Data Connectivity. Integrate Power BI XMLA with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to use SQLAlchemy ORM to access Power BI XMLA Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Power BI XMLA data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Power BI XMLA and the SQLAlchemy toolkit, you can build Power BI XMLA-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Power BI XMLA data to query 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 CData Connector 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.

Follow the procedure below to install SQLAlchemy and start accessing Power BI XMLA through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Power BI XMLA Data in Python

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

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("powerbixmla:///?AuthScheme=AzureADInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Power BI XMLA Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Customer table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Customer(base): __tablename__ = "Customer" Country = Column(String,primary_key=True) Education = Column(String) ...

Query Power BI XMLA Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("powerbixmla:///?AuthScheme=AzureADInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(Country="Australia"): print("Country: ", instance.Country) print("Education: ", instance.Education) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Customer_table = Customer.metadata.tables["Customer"] for instance in session.execute( == "Australia")): print("Country: ", instance.Country) print("Education: ", instance.Education) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

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.