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Use SQLAlchemy ORMs to Access Salesforce Marketing Data in Python

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

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

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

Connecting to Salesforce Marketing Data

Connecting to Salesforce Marketing 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.

Authenticating to the Salesforce Marketing Cloud APIs

Set the User and Password to your login credentials, or to the credentials for a sandbox user if you are connecting to a sandbox account.

Connecting to the Salesforce Marketing Cloud APIs

By default, the data provider connects to production environments. Set UseSandbox to true to use a Salesforce Marketing Cloud sandbox account.

The default Instance is s7 of the Web Services API; however, if you use a different instance, you can set Instance.

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

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

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

Declare a Mapping Class for Salesforce Marketing 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 Subscriber 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 Subscriber(base):
	__tablename__ = "Subscriber"
	Id = Column(String,primary_key=True)
	Status = Column(String)
	...

Query Salesforce Marketing 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("sfmarketingcloud///?User=myUser&Password=myPassword&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Subscriber).filter_by(EmailAddress="john.doe@example.com"):
	print("Id: ", instance.Id)
	print("Status: ", instance.Status)
	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

Subscriber_table = Subscriber.metadata.tables["Subscriber"]
for instance in session.execute(Subscriber_table.select().where(Subscriber_table.c.EmailAddress == "john.doe@example.com")):
	print("Id: ", instance.Id)
	print("Status: ", instance.Status)
	print("---------")

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

Insert Salesforce Marketing Data

To insert Salesforce Marketing 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 Salesforce Marketing.

new_rec = Subscriber(Id="placeholder", EmailAddress="john.doe@example.com")
session.add(new_rec)
session.commit()

Update Salesforce Marketing Data

To update Salesforce Marketing 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 Salesforce Marketing.

updated_rec = session.query(Subscriber).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.EmailAddress = "john.doe@example.com"
session.commit()

Delete Salesforce Marketing Data

To delete Salesforce Marketing 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(Subscriber).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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