Use SQLAlchemy ORMs to Access 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 Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Azure Analysis Services data.

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

Follow the procedure below to install SQLAlchemy and start accessing Azure Analysis Services 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 Azure Analysis Services Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Azure Analysis Services 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("aas:///?URL=asazure://")

Declare a Mapping Class for Azure Analysis Services 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 Azure Analysis Services 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("aas:///?URL=asazure://") 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 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.