How to Build an ETL App for Salesforce Data in Python with CData

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).

Getting Started

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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: OAuth, Login (or basic), and SSO. The Login method requires you to have the username, password, and security token of the user.

OAuth Authentication (default)

The default authentication mechanism (and the one preferred by Salesforce) is OAuth. To use OAuth with CData's embedded OAuth application, leave the connection properties blank. If you have configured your own custom OAuth application with Salesforce (see the Help documentation for more information), set OAuthClientId, OAuthClientSecret, and CallbackURL to the properties for you application. Set InitiateOAuth to the desired OAuth flow ("GETANDREFRESH" will have the connector manage the entire OAuth flow).

Login (or Basic) Authentication

If you do not wish do not wish to use OAuth authentication, you can use Login (or basic) authentication. Set AuthScheme to Basic, and set the User, Password, and SecurityToken properties. You can configure your security token in Salesforce.

SSO (single sign-on) Authentication

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

Multi-Factor Authentication (MFA)

If your Salesforce org has MFA enforcement enabled, set MFACode to the time-based one-time passcode (TOTP) generated by your authenticator app (such as Salesforce Authenticator or Google Authenticator). MFACode applies to both OAuth and Login authentication flows.

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("InitiateOAuth=GETANDREFRESH;MFACode=YourMFACode")

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 CData Python Connector for Salesforce to start building Python apps and scripts with connectivity to Salesforce data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;MFACode=YourMFACode")

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')