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

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

Getting Started

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Connecting to SAP Data

Connecting to SAP 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 connect to SAP systems using either librfc32.dll, librfc32u.dll, NetWeaver, or Web Services (SOAP). Set the ConnectionType connection property to CLASSIC (librfc32.dll), CLASSIC_UNICODE (librfc32u.dll), NETWEAVER, or SOAP.

If you are using the SOAP interface, set the Client, RFCUrl, SystemNumber, User, and Password properties, under the Authentication section.

Otherwise, set Host, User, Password, Client, and SystemNumber.

Note: We do not distribute the librfc32.dll or other SAP assemblies. You must find them from your SAP installation and install them on your machine.

For more information, see this guide on obtaining the connection properties needed to connect to any SAP system.

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

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

cnxn = mod.connect("Host=sap.mydomain.com;User=EXT90033;Password=xxx;Client=800;System Number=09;ConnectionType=Classic;Location=C:/mysapschemafolder;")

Create a SQL Statement to Query SAP

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

sql = "SELECT MANDT, MBRSH FROM MARA WHERE ERNAM = 'BEHRMANN'"

Extract, Transform, and Load the SAP Data

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

Loading SAP Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("Host=sap.mydomain.com;User=EXT90033;Password=xxx;Client=800;System Number=09;ConnectionType=Classic;Location=C:/mysapschemafolder;")

sql = "SELECT MANDT, MBRSH FROM MARA WHERE ERNAM = 'BEHRMANN'"

table1 = etl.fromdb(cnxn,sql)

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

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