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Extract, Transform, and Load Salesforce Marketing Data in Python

The CData Python Connector for Salesforce Marketing enables you to create ETL applications and pipelines for Salesforce Marketing data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Salesforce Marketing and the petl framework, you can build Salesforce Marketing-connected applications and pipelines for extracting, transforming, and loading Salesforce Marketing data. This article shows how to connect to Salesforce Marketing with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Salesforce Marketing Connector, follow the procedure below to install the other required modules and start accessing Salesforce Marketing through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Salesforce Marketing Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.sfmarketingcloud as mod

You can now connect with a connection string. Use the connect function for the CData Salesforce Marketing Connector to create a connection for working with Salesforce Marketing data.

cnxn = mod.connect("User=myUser;Password=myPassword;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Salesforce Marketing

Use SQL to create a statement for querying Salesforce Marketing. In this article, we read data from the Subscriber entity.

sql = "SELECT Id, Status FROM Subscriber WHERE EmailAddress = 'john.doe@example.com'"

Extract, Transform, and Load the Salesforce Marketing Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Salesforce Marketing data. In this example, we extract Salesforce Marketing data, sort the data by the Status column, and load the data into a CSV file.

Loading Salesforce Marketing Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Status')

etl.tocsv(table2,'subscriber_data.csv')

In the following example, we add new rows to the Subscriber table.

Adding New Rows to Salesforce Marketing

table1 = [ ['Id','Status'], ['NewId1','NewStatus1'], ['NewId2','NewStatus2'], ['NewId3','NewStatus3'] ]

etl.appenddb(table1, cnxn, 'Subscriber')

With the CData Python Connector for Salesforce Marketing, you can work with Salesforce Marketing data just like you would with any database, including direct access to data in ETL packages like petl.

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.



Full Source Code


import petl as etl
import pandas as pd
import cdata.sfmarketingcloud as mod

cnxn = mod.connect("User=myUser;Password=myPassword;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Status FROM Subscriber WHERE EmailAddress = 'john.doe@example.com'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Status')

etl.tocsv(table2,'subscriber_data.csv')

table3 = [ ['Id','Status'], ['NewId1','NewStatus1'], ['NewId2','NewStatus2'], ['NewId3','NewStatus3'] ]

etl.appenddb(table3, cnxn, 'Subscriber')