Use SQLAlchemy ORMs to Access SQL Analysis Services Data in Python

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SQL Analysis Services Python Connector

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

The CData Python Connector for SQL Analysis Services enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of SQL 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 SQL Analysis Services and the SQLAlchemy toolkit, you can build SQL Analysis Services-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to SQL Analysis Services data to query SQL Analysis Services data.

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

Connecting to SQL Analysis Services Data

Connecting to SQL 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, provide authentication and set the Url property to a valid SQL Server Analysis Services endpoint. You can connect to SQL Server Analysis Services instances hosted over HTTP with XMLA access. See the Microsoft documentation to configure HTTP access to SQL Server Analysis Services.

To secure connections and authenticate, set the corresponding connection properties, below. The data provider supports the major authentication schemes, including HTTP and Windows, as well as SSL/TLS.

  • HTTP Authentication

    Set AuthScheme to "Basic" or "Digest" and set User and Password. Specify other authentication values in CustomHeaders.

  • Windows (NTLM)

    Set the Windows User and Password and set AuthScheme to "NTLM".

  • Kerberos and Kerberos Delegation

    To authenticate with Kerberos, set AuthScheme to NEGOTIATE. To use Kerberos delegation, set AuthScheme to KERBEROSDELEGATION. If needed, provide the User, Password, and KerberosSPN. By default, the data provider attempts to communicate with the SPN at the specified Url.

  • SSL/TLS:

    By default, the data provider attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

You can then access any cube as a relational table: When you connect the data provider retrieves SSAS metadata and dynamically updates the table schemas. Instead of retrieving metadata every connection, you can set the CacheLocation property to automatically cache to a simple file-based store.

See the Getting Started section of the CData documentation, under Retrieving Analysis Services Data, to execute SQL-92 queries to the cubes.

Follow the procedure below to install SQLAlchemy and start accessing SQL Analysis Services through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model SQL 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 SQL Analysis Services data.

engine = create_engine("ssas:///?User=myuseraccount&Password=mypassword&URL=http://localhost/OLAP/msmdpump.dll")

Declare a Mapping Class for SQL 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 Adventure_Works 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 Adventure_Works(base):
	__tablename__ = "Adventure_Works"
	Fiscal_Year = Column(String,primary_key=True)
	Sales_Amount = Column(String)

Query SQL 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("ssas:///?User=myuseraccount&Password=mypassword&URL=http://localhost/OLAP/msmdpump.dll")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Adventure_Works).filter_by(Fiscal_Year="FY 2008"):
	print("Fiscal_Year: ", instance.Fiscal_Year)
	print("Sales_Amount: ", instance.Sales_Amount)

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

Using the execute Method

Adventure_Works_table = Adventure_Works.metadata.tables["Adventure_Works"]
for instance in session.execute( == "FY 2008")):
	print("Fiscal_Year: ", instance.Fiscal_Year)
	print("Sales_Amount: ", instance.Sales_Amount)

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 SQL Analysis Services Python Connector to start building Python apps and scripts with connectivity to SQL Analysis Services data. Reach out to our Support Team if you have any questions.