Use SQLAlchemy ORMs to Access Sage US Data in Python

Ready to get started?

Learn more:

Sage US Connectivity Solutions

The CData Python Connector for Sage US enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Sage US data.

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

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

Connecting to Sage US Data

Connecting to Sage US 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.

The Application Id and Company Name connection string options are required to connect to Sage as a data source. You can obtain an Application Id by contacting Sage directly to request access to the Sage 50 SDK.

Sage must be installed on the machine. The Sage.Peachtree.API.dll and Sage.Peachtree.API.Resolver.dll assemblies are required. These assemblies are installed with Sage in C:\Program Files\Sage\Peachtree\API\. Additionally, the Sage SDK requires .NET Framework 4.0 and is only compatible with 32-bit applications. To use the Sage SDK in Visual Studio, set the Platform Target property to "x86" in Project -> Properties -> Build.

You must authorize the application to access company data: To authorize your application to access Sage, restart the Sage application, open the company you want to access, and connect with your application. You will then be prompted to set access permissions for the application in the resulting dialog.

While the compiled executable will require authorization only once, during development you may need to follow this process to reauthorize a new build. To avoid restarting the Sage application when developing with Visual Studio, click Build -> Configuration Manager and uncheck "Build" for your project.

Follow the procedure below to install SQLAlchemy and start accessing Sage US 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 Sage US Data in Python

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

engine = create_engine("sage50us///?ApplicationId=8dfafu4V4ODmh1fM0xx&CompanyName=Bellwether Garden Supply - Premium")

Declare a Mapping Class for Sage US 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"
	Name = Column(String,primary_key=True)
	LastInvoiceAmount = Column(String)

Query Sage US 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("sage50us///?ApplicationId=8dfafu4V4ODmh1fM0xx&CompanyName=Bellwether Garden Supply - Premium")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customer).filter_by(Name="ALDRED"):
	print("Name: ", instance.Name)
	print("LastInvoiceAmount: ", instance.LastInvoiceAmount)

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( == "ALDRED")):
	print("Name: ", instance.Name)
	print("LastInvoiceAmount: ", instance.LastInvoiceAmount)

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

Insert Sage US Data

To insert Sage US data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Sage US.

new_rec = Customer(Name="placeholder", Name="ALDRED")

Update Sage US Data

To update Sage US data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Sage US.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "ALDRED"

Delete Sage US Data

To delete Sage US data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided recoreds (rows).

deleted_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

Download a free, 30-day trial of the Sage US Python Connector to start building Python apps and scripts with connectivity to Sage US data. Reach out to our Support Team if you have any questions.