How to use SQLAlchemy ORM to access SAP Business Warehouse Data in Python

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

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

Connecting to SAP Business Warehouse Data

Connecting to SAP Business Warehouse 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 SAP Business Warehouse, set the URL property to a valid SAP Business Warehouse server base URL. The driver must connect to SAP Business Warehouse instances hosted over HTTP with XMLA access.

The driver supports the following authentication schemes via the AuthScheme property:

• None: Anonymous authentication, if available on the server.
• Basic: Set User and Password and set AuthScheme to Basic.
• Kerberos: See the Using Kerberos section of the help documentation for the required Kerberos properties.

By default, the driver 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.

Follow the procedure below to install SQLAlchemy and start accessing SAP Business Warehouse 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 SAP Business Warehouse Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SAP Business Warehouse 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("sapbusinesswarehouse:///?URL=https://mysapserver:8000&AuthScheme=Basic&User=username&Password=password")

Declare a Mapping Class for SAP Business Warehouse 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 Sales 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 Sales(base):
	__tablename__ = "Sales"
	CustomerCount = Column(String,primary_key=True)
	City = Column(String)
	...

Query SAP Business Warehouse 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("sapbusinesswarehouse:///?URL=https://mysapserver:8000&AuthScheme=Basic&User=username&Password=password")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Sales).filter_by(Country="US"):
	print("CustomerCount: ", instance.CustomerCount)
	print("City: ", instance.City)
	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

Sales_table = Sales.metadata.tables["Sales"]
for instance in session.execute(Sales_table.select().where(Sales_table.c.Country == "US")):
	print("CustomerCount: ", instance.CustomerCount)
	print("City: ", instance.City)
	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 SAP Business Warehouse to start building Python apps and scripts with connectivity to SAP Business Warehouse data. Reach out to our Support Team if you have any questions.