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Use SQLAlchemy ORMs to Access FinancialForce Data in Python

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

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

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

Connecting to FinancialForce Data

Connecting to FinancialForce 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.

There are several authentication methods available for connecting to FinancialForce: login credentials, SSO, and OAuth.

Authenticating with a Login and Token

Set the User and Password to your login credentials. Additionally, set the SecurityToken. By default, the SecurityToken is required, but you can make it optional by allowing a range of trusted IP addresses.

To disable the security token:

  1. Log in to FinancialForce and enter "Network Access" in the Quick Find box in the setup section.
  2. Add your IP address to the list of trusted IP addresses.

To obtain the security token:

  1. Open the personal information page on
  2. Click the link to reset your security token. The token will be emailed to you.
  3. Specify the security token in the SecurityToken connection property or append it to the Password.

Authenticating with OAuth

If you do not have access to the user name and password or do not want to require them, use the OAuth user consent flow. See the OAuth section in the Help for an authentication guide.

Connecting to FinancialForce Sandbox Accounts

Set UseSandbox to true (false by default) to use a FinancialForce sandbox account. Ensure that you specify a sandbox user name in User.

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

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

engine = create_engine("financialforce///?User=myUser&Password=myPassword&Security Token=myToken&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for FinancialForce 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 Account 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 Account(base):
	__tablename__ = "Account"
	BillingState = Column(String,primary_key=True)
	Name = Column(String)

Query FinancialForce 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("financialforce///?User=myUser&Password=myPassword&Security Token=myToken&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Account).filter_by(Industry="Floppy Disks"):
	print("BillingState: ", instance.BillingState)
	print("Name: ", instance.Name)

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

Using the execute Method

Account_table = Account.metadata.tables["Account"]
for instance in session.execute( == "Floppy Disks")):
	print("BillingState: ", instance.BillingState)
	print("Name: ", instance.Name)

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

Insert FinancialForce Data

To insert FinancialForce 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 FinancialForce.

new_rec = Account(BillingState="placeholder", Industry="Floppy Disks")

Update FinancialForce Data

To update FinancialForce 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 FinancialForce.

updated_rec = session.query(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Industry = "Floppy Disks"

Delete FinancialForce Data

To delete FinancialForce 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(Account).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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