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Use SQLAlchemy ORMs to Access Salesforce Pardot Data in Python

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

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

Connecting to Salesforce Pardot Data

Connecting to Salesforce Pardot 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.

Salesforce Pardot supports connecting through API Version, Username, Password and User Key.

  • ApiVersion: The Salesforce Pardot API version which the provided account can access. Defaults to 4.
  • User: The Username of the Salesforce Pardot account.
  • Password: The Password of the Salesforce Pardot account.
  • UserKey: The unique User Key for the Salesforce Pardot account. This key does not expire.
  • IsDemoAccount (optional): Set to TRUE to connect to a demo account.

Accessing the Pardot User Key

The User Key of the current account may be accessed by going to Settings -> My Profile, under the API User Key row.

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

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

engine = create_engine("salesforcepardot///?ApiVersion=4&User=YourUsername&Password=YourPassword&UserKey=YourUserKey")

Declare a Mapping Class for Salesforce Pardot 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 Prospects 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 Prospects(base):
	__tablename__ = "Prospects"
	Id = Column(String,primary_key=True)
	Email = Column(String)
	...

Query Salesforce Pardot 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("salesforcepardot///?ApiVersion=4&User=YourUsername&Password=YourPassword&UserKey=YourUserKey")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Prospects).filter_by(ProspectAccountId="703"):
	print("Id: ", instance.Id)
	print("Email: ", instance.Email)
	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

Prospects_table = Prospects.metadata.tables["Prospects"]
for instance in session.execute(Prospects_table.select().where(Prospects_table.c.ProspectAccountId == "703")):
	print("Id: ", instance.Id)
	print("Email: ", instance.Email)
	print("---------")

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

Insert Salesforce Pardot Data

To insert Salesforce Pardot 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 Pardot.

new_rec = Prospects(Id="placeholder", ProspectAccountId="703")
session.add(new_rec)
session.commit()

Update Salesforce Pardot Data

To update Salesforce Pardot 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 Pardot.

updated_rec = session.query(Prospects).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ProspectAccountId = "703"
session.commit()

Delete Salesforce Pardot Data

To delete Salesforce Pardot 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(Prospects).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 Pardot Python Connector to start building Python apps and scripts with connectivity to Salesforce Pardot data. Reach out to our Support Team if you have any questions.