How to Build an ETL App for SAP SuccessFactors LMS Data in Python with CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK and the petl framework, you can build SAP SuccessFactors LMS-connected applications and pipelines for extracting, transforming, and loading SAP SuccessFactors LMS data. This article shows how to connect to Connect AI and use petl to extract, transform, and load SAP SuccessFactors LMS data.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so petl can read directly from the SDK connection with etl.fromdb. There is no driver to install per source: connect with a Personal Access Token and build your pipeline.

Connect to SAP SuccessFactors LMS in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SAP SuccessFactors LMS" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAP SuccessFactors LMS.

   SAP SuccessFactors LMS uses OAuth authentication. Before connecting, you must configure an OAuth application tied to your SAP SuccessFactors LMS account.

   To establish a connection, set the following properties:

   • User: Your SAP SuccessFactors LMS username.
   • CompanyId: Your SAP SuccessFactors company identifier.
   • Url: The SAP SuccessFactors API URL (e.g., https://api4.successfactors.com).
   • OAuthClientId: The client Id assigned when you registered your custom OAuth application.
   • OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.

   See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK and the petl framework using the pip utility:

pip install cdata-connect-ai
pip install petl

Build an ETL App for SAP SuccessFactors LMS Data in Python

Once the required modules are installed, you are ready to build the ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, import the modules and connect to Connect AI with your account email and PAT:

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Create a SQL Statement to Query SAP SuccessFactors LMS

Use SQL to create a statement for querying SAP SuccessFactors LMS. In this article, we read data from the Items entity. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SAPSuccessFactorsLMS1).

sql = (
    "SELECT ItemID, ItemTitle "
    "FROM [SAPSuccessFactorsLMS1].[SAPSuccessFactorsLMS].[Items] "
    "WHERE Active = 'true'"
)

Extract, Transform, and Load the SAP SuccessFactors LMS Data

With a connection and query in hand, use petl to extract, transform, and load the SAP SuccessFactors LMS data. In this example, we extract SAP SuccessFactors LMS data, sort the data by the ItemTitle column, and load the data into a CSV file.

table1 = etl.fromdb(conn, sql)

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

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

SAP SuccessFactors LMS is a read-only source in Connect AI, so this pipeline can extract and transform SAP SuccessFactors LMS data but not load rows back. Close the connection when the extract is complete:

conn.close()

With the CData Connect AI Python SDK, you can work with SAP SuccessFactors LMS data just like you would with any database, including direct access to data in ETL packages like petl.

More Information and Free Trial

Now you can pipe live SAP SuccessFactors LMS data through petl using the CData Connect AI Python SDK. For more information on connecting to SAP SuccessFactors LMS (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start building data pipelines for live SAP SuccessFactors LMS data in Python.

Full Source Code

import petl as etl
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

sql = (
    "SELECT ItemID, ItemTitle "
    "FROM [SAPSuccessFactorsLMS1].[SAPSuccessFactorsLMS].[Items] "
    "WHERE Active = 'true'"
)

table1 = etl.fromdb(conn, sql)

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

etl.tocsv(table2, 'items_data.csv')
conn.close()