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Extract, Transform, and Load SAP SuccessFactors Data in Python

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

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

Connecting to SAP SuccessFactors Data

Connecting to SAP SuccessFactors 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.

You can authenticate to SAP Success Factors using Basic authentication or OAuth with SAML assertion.

Basic Authentication

You must provide values for the following properties to successfully authenticate to SAP Success Factors. Note that the provider will reuse the session opened by SAP Success Factors using cookies. Which means that your credentials will be used only on the first request to open the session. After that, cookies returned from SAP Success Factors will be used for authentication.

  • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
  • User: set this to the username of your account.
  • Password: set this to the password of your account.
  • CompanyId: set this to the unique identifier of your company.

OAuth Authentication

You must provide values for the following properties, which will be used to get the access token.

  • Url: set this to the URL of the server hosting Success Factors. Some of the servers are listed in the SAP support documentation (external link).
  • User: set this to the username of your account.
  • CompanyId: set this to the unique identifier of your company.
  • OAuthClientId: set this to the API Key that was generated in API Center.
  • OAuthClientSecret: the X.509 private key used to sign SAML assertion. The private key can be found in the certificate you downloaded in Registering your OAuth Client Application.
  • InitiateOAuth: set this to GETANDREFRESH.

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

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

cnxn = mod.connect("User=username;Password=password;CompanyId=CompanyId;Url=https://api4.successfactors.com;")

Create a SQL Statement to Query SAP SuccessFactors

Use SQL to create a statement for querying SAP SuccessFactors. In this article, we read data from the ExtAddressInfo entity.

sql = "SELECT address1, zipCode FROM ExtAddressInfo WHERE city = 'Springfield'"

Extract, Transform, and Load the SAP SuccessFactors Data

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

Loading SAP SuccessFactors Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SAP SuccessFactors

table1 = [ ['address1','zipCode'], ['Newaddress11','NewzipCode1'], ['Newaddress12','NewzipCode2'], ['Newaddress13','NewzipCode3'] ]

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

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

cnxn = mod.connect("User=username;Password=password;CompanyId=CompanyId;Url=https://api4.successfactors.com;")

sql = "SELECT address1, zipCode FROM ExtAddressInfo WHERE city = 'Springfield'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['address1','zipCode'], ['Newaddress11','NewzipCode1'], ['Newaddress12','NewzipCode2'], ['Newaddress13','NewzipCode3'] ]

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