How to Build an ETL App for Salesforce Data 360 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 Data 360 and the petl framework, you can build Salesforce Data 360-connected applications and pipelines for extracting, transforming, and loading Salesforce Data 360 data. This article shows how to connect to Salesforce Data 360 with the CData Python Connector and use petl and pandas to extract, transform, and load Salesforce Data 360 data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Salesforce Data 360 data in Python. When you issue complex SQL queries from Salesforce Data 360, the driver pushes supported SQL operations, like filters and aggregations, directly to Salesforce Data 360 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Salesforce Data 360 Data

Connecting to Salesforce Data 360 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 Data 360 supports authentication via the OAuth standard.

OAuth

Set AuthScheme to OAuth.

Desktop Applications

CData provides an embedded OAuth application that simplifies authentication at the desktop.

You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Salesforce Data 360 console. For further information, see Creating a Custom OAuth App in the Help documentation.

Before you connect, set these properties:

• InitiateOAuth: GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• OAuthClientId (custom applications only): The Client ID assigned when you registered your custom OAuth application.
• OAuthClientSecret (custom applications only): The Client Secret assigned when you registered your custom OAuth application.

When you connect, the driver opens Salesforce Data 360's OAuth endpoint in your default browser. Log in and grant permissions to the application.

The driver then completes the OAuth process as follows:

• Extracts the access token from the callback URL.
• Obtains a new access token when the old one expires.
• Saves OAuth values in OAuthSettingsLocation so that they persist across connections.

For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

After installing the CData Salesforce Data 360 Connector, follow the procedure below to install the other required modules and start accessing Salesforce Data 360 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 360 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.salesforcedatacloud as mod

You can now connect with a connection string. Use the connect function for the CData Salesforce Data 360 Connector to create a connection for working with Salesforce Data 360 data.

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

Create a SQL Statement to Query Salesforce Data 360

Use SQL to create a statement for querying Salesforce Data 360. In this article, we read data from the Account entity.

sql = "SELECT [Account ID], [Account Name] FROM Account WHERE EmployeeCount = '250'"

Extract, Transform, and Load the Salesforce Data 360 Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Salesforce Data 360 data. In this example, we extract Salesforce Data 360 data, sort the data by the [Account Name] column, and load the data into a CSV file.

Loading Salesforce Data 360 Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[Account Name]')

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

With the CData Python Connector for Salesforce Data 360, you can work with Salesforce Data 360 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 Data 360 to start building Python apps and scripts with connectivity to Salesforce Data 360 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.salesforcedatacloud as mod

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

sql = "SELECT [Account ID], [Account Name] FROM Account WHERE EmployeeCount = '250'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[Account Name]')

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