![]() The methods of data collection may depend on the nature of the data source, and the process of managing this data and making it usable for analysis often involves ETL (Extract, Transform, Load). Reach out to our Support Team if you have any questions.Ĭnxn = mod.connect("Host= can come from many different sources - it might be generated by users, collected from sensors, retrieved from databases, even scraped from websites. Free Trial & More Informationĭownload a free, 30-day trial of the SAP Python Connector to start building Python apps and scripts with connectivity to SAP data. With the CData Python Connector for SAP, you can work with SAP data just like you would with any database, including direct access to data in ETL packages like petl. In this example, we extract SAP data, sort the data by the MBRSH column, and load the data into a CSV file. ![]() With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SAP data. Sql = "SELECT MANDT, MBRSH FROM MARA WHERE ERNAM = 'BEHRMANN'"Įxtract, Transform, and Load the SAP Data In this article, we read data from the MARA entity. Use SQL to create a statement for querying SAP. Use the connect function for the CData SAP Connector to create a connection for working with SAP data.Ĭnxn = mod.connect("Host= User=EXT90033 Password=xxx Client=800 System Number=09 ConnectionType=Classic Location=C:/mysapschemafolder ") You can now connect with a connection string. ![]() Code snippets follow, but the full source code is available at the end of the article.įirst, be sure to import the modules (including the CData Connector) with the following: Once the required modules and frameworks are installed, we are ready to build our ETL app. Pip install pandas Build an ETL App for SAP Data in Python ![]() Use the pip utility to install the required modules and frameworks: pip install petl You must find them from your SAP installation and install them on your machine.įor more information, see this guide on obtaining the connection properties needed to connect to any SAP system.Īfter installing the CData SAP Connector, follow the procedure below to install the other required modules and start accessing SAP through Python objects. Note: We do not distribute the librfc32.dll or other SAP assemblies. Otherwise, set Host, User, Password, Client, and SystemNumber. Properties, under the Authentication section. If you are using the SOAP interface, set the Client, RFCUrl, SystemNumber, User, and Password Set the ConnectionType connection property to CLASSIC (librfc32.dll), CLASSIC_UNICODE (librfc32u.dll), NETWEAVER, or SOAP. You can connect to SAP systems using either librfc32.dll, librfc32u.dll, NetWeaver, or Web Services (SOAP). For this article, you will pass the connection string as a parameter to the create_engine function. Create a connection string using the required connection properties. 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).Ĭonnecting to SAP data looks just like connecting to any relational data source. With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live SAP data in Python. 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 the CData Python Connector for SAP and the petl framework, you can build SAP-connected applications and pipelines for extracting, transforming, and loading SAP data. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
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