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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 LMS 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 SAP SuccessFactors LMS with the CData Python Connector and use petl and pandas to extract, transform, and load SAP SuccessFactors LMS data.
With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live SAP SuccessFactors LMS data in Python. When you issue complex SQL queries from SAP SuccessFactors LMS, the driver pushes supported SQL operations, like filters and aggregations, directly to SAP SuccessFactors LMS and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to SAP SuccessFactors LMS 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.
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:
See the Getting Started chapter of the help documentation for a guide to creating a custom OAuth app and using OAuth.
After installing the CData SAP SuccessFactors LMS Connector, follow the procedure below to install the other required modules and start accessing SAP SuccessFactors LMS through Python objects.
Use the pip utility to install the required modules and frameworks:
pip install petl pip install pandas
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.sapsuccessfactorslms as mod
You can now connect with a connection string. Use the connect function for the CData SAP SuccessFactors LMS Connector to create a connection for working with SAP SuccessFactors LMS data.
cnxn = mod.connect("User=username;CompanyId=CompanyId;Url=https://api4.successfactors.com;InitiateOAuth=GETANDREFRESH;")
Use SQL to create a statement for querying SAP SuccessFactors LMS. In this article, we read data from the Items entity.
sql = "SELECT ItemID, ItemTitle FROM Items WHERE Active = 'true'"
With the query results stored in a DataFrame, we can 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(cnxn,sql) table2 = etl.sort(table1,'ItemTitle') etl.tocsv(table2,'items_data.csv')
With the CData Python Connector for SAP SuccessFactors LMS, 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.
Download a free, 30-day trial of the CData Python Connector for SAP SuccessFactors LMS to start building Python apps and scripts with connectivity to SAP SuccessFactors LMS data. Reach out to our Support Team if you have any questions.
import petl as etl
import pandas as pd
import cdata.sapsuccessfactorslms as mod
cnxn = mod.connect("User=username;CompanyId=CompanyId;Url=https://api4.successfactors.com;InitiateOAuth=GETANDREFRESH;")
sql = "SELECT ItemID, ItemTitle FROM Items WHERE Active = 'true'"
table1 = etl.fromdb(cnxn,sql)
table2 = etl.sort(table1,'ItemTitle')
etl.tocsv(table2,'items_data.csv')
Download a free trial of the SAP SuccessFactors LMS Connector to get started:
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👁 SAP SuccessFactors LMS IconPython Connector Libraries for SAP SuccessFactors LMS Data Connectivity. Integrate SAP SuccessFactors LMS with popular Python tools like Pandas, SQLAlchemy, Dash & petl.