How to Build an ETL App for SAP Hybris C4C 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 SAP Hybris C4C and the petl framework, you can build SAP Hybris C4C-connected applications and pipelines for extracting, transforming, and loading SAP Hybris C4C data. This article shows how to connect to SAP Hybris C4C with the CData Python Connector and use petl and pandas to extract, transform, and load SAP Hybris C4C data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live SAP Hybris C4C data in Python. When you issue complex SQL queries from SAP Hybris C4C, the driver pushes supported SQL operations, like filters and aggregations, directly to SAP Hybris C4C and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to SAP Hybris C4C Data

Connecting to SAP Hybris C4C 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 Hybris Cloud for Customer uses basic authentication. Set the User and Password to your login credentials.

After installing the CData SAP Hybris C4C Connector, follow the procedure below to install the other required modules and start accessing SAP Hybris C4C 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 Hybris C4C 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.saphybrisc4c as mod

You can now connect with a connection string. Use the connect function for the CData SAP Hybris C4C Connector to create a connection for working with SAP Hybris C4C data.

cnxn = mod.connect("User=user;Password=password;")

Create a SQL Statement to Query SAP Hybris C4C

Use SQL to create a statement for querying SAP Hybris C4C. In this article, we read data from the AccountCollection entity.

sql = "SELECT ObjectID, AccountName FROM AccountCollection WHERE AccountName = 'MyAccount'"

Extract, Transform, and Load the SAP Hybris C4C Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SAP Hybris C4C data. In this example, we extract SAP Hybris C4C data, sort the data by the AccountName column, and load the data into a CSV file.

Loading SAP Hybris C4C Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SAP Hybris C4C

table1 = [ ['ObjectID','AccountName'], ['NewObjectID1','NewAccountName1'], ['NewObjectID2','NewAccountName2'], ['NewObjectID3','NewAccountName3'] ]

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

With the CData Python Connector for SAP Hybris C4C, you can work with SAP Hybris C4C 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 SAP Hybris C4C to start building Python apps and scripts with connectivity to SAP Hybris C4C 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.saphybrisc4c as mod

cnxn = mod.connect("User=user;Password=password;")

sql = "SELECT ObjectID, AccountName FROM AccountCollection WHERE AccountName = 'MyAccount'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ObjectID','AccountName'], ['NewObjectID1','NewAccountName1'], ['NewObjectID2','NewAccountName2'], ['NewObjectID3','NewAccountName3'] ]

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