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Use SQLAlchemy ORMs to Access Marketo Data in Python

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

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

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

Connecting to Marketo Data

Connecting to Marketo 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.

Both the REST and SOAP APIs are supported and can be chosen by using the Schema property.

For the REST API: The OAuthClientId, OAuthClientSecret, and RESTEndpoint properties, under the OAuth and REST Connection sections, must be set to valid Marketo user credentials.

For the SOAP API: The UserId, EncryptionKey, and SOAPEndpoint properties, under the SOAP Connection section, must be set to valid Marketo user credentials.

See the "Getting Started" chapter of the help documentation for a guide to obtaining these values.

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

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

engine = create_engine("marketo///?Schema=REST&RESTEndpoint=")

Declare a Mapping Class for Marketo 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 Leads 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 Leads(base):
	__tablename__ = "Leads"
	Email = Column(String,primary_key=True)
	AnnualRevenue = Column(String)

Query Marketo 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("marketo///?Schema=REST&RESTEndpoint=")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Leads).filter_by(Country="U.S.A."):
	print("Email: ", instance.Email)
	print("AnnualRevenue: ", instance.AnnualRevenue)

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

Using the execute Method

Leads_table = Leads.metadata.tables["Leads"]
for instance in session.execute( == "U.S.A.")):
	print("Email: ", instance.Email)
	print("AnnualRevenue: ", instance.AnnualRevenue)

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

Insert Marketo Data

To insert Marketo 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 Marketo.

new_rec = Leads(Email="placeholder", Country="U.S.A.")

Update Marketo Data

To update Marketo 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 Marketo.

updated_rec = session.query(Leads).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Country = "U.S.A."

Delete Marketo Data

To delete Marketo 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(Leads).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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