Extract, Transform, and Load Salesforce Data in Python

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Salesforce Python Connector

Python Connector Libraries for Salesforce Data Connectivity. Integrate Salesforce with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Salesforce Data

Connecting to Salesforce 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.

There are several authentication methods available for connecting to Salesforce: Login, OAuth, and SSO. The Login method requires you to have the username, password, and security token of the user.

If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication.

SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and TokenUrl connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the help documentation for more information.

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

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

cnxn = mod.connect("User=username;Password=password;SecurityToken=Your_Security_Token;")

Create a SQL Statement to Query Salesforce

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

sql = "SELECT Industry, AnnualRevenue FROM Account WHERE Name = 'GenePoint'"

Extract, Transform, and Load the Salesforce Data

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

Loading Salesforce Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Salesforce

table1 = [ ['Industry','AnnualRevenue'], ['NewIndustry1','NewAnnualRevenue1'], ['NewIndustry2','NewAnnualRevenue2'], ['NewIndustry3','NewAnnualRevenue3'] ]

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

With the CData Python Connector for Salesforce, you can work with Salesforce 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 Python Connector to start building Python apps and scripts with connectivity to Salesforce 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.salesforce as mod

cnxn = mod.connect("User=username;Password=password;SecurityToken=Your_Security_Token;")

sql = "SELECT Industry, AnnualRevenue FROM Account WHERE Name = 'GenePoint'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Industry','AnnualRevenue'], ['NewIndustry1','NewAnnualRevenue1'], ['NewIndustry2','NewAnnualRevenue2'], ['NewIndustry3','NewAnnualRevenue3'] ]

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