Ready to get started?

Learn more about the CData Python Connector for Salesforce Pardot or download a free trial:

Download Now

Extract, Transform, and Load Salesforce Pardot Data in Python

The CData Python Connector for Salesforce Pardot enables you to create ETL applications and pipelines for Salesforce Pardot 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 Pardot and the petl framework, you can build Salesforce Pardot-connected applications and pipelines for extracting, transforming, and loading Salesforce Pardot data. This article shows how to connect to Salesforce Pardot with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Salesforce Pardot Connector, follow the procedure below to install the other required modules and start accessing Salesforce Pardot 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 Pardot 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.salesforcepardot as mod

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

cnxn = mod.connect("ApiVersion=4;User=YourUsername;Password=YourPassword;UserKey=YourUserKey;")

Create a SQL Statement to Query Salesforce Pardot

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

sql = "SELECT Id, Email FROM Prospects WHERE ProspectAccountId = '703'"

Extract, Transform, and Load the Salesforce Pardot Data

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

Loading Salesforce Pardot Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Salesforce Pardot

table1 = [ ['Id','Email'], ['NewId1','NewEmail1'], ['NewId2','NewEmail2'], ['NewId3','NewEmail3'] ]

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

With the CData Python Connector for Salesforce Pardot, you can work with Salesforce Pardot 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 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.



Full Source Code


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

cnxn = mod.connect("ApiVersion=4;User=YourUsername;Password=YourPassword;UserKey=YourUserKey;")

sql = "SELECT Id, Email FROM Prospects WHERE ProspectAccountId = '703'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Email'], ['NewId1','NewEmail1'], ['NewId2','NewEmail2'], ['NewId3','NewEmail3'] ]

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