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

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

Connecting to SAP Ariba Source Data

Connecting to SAP Ariba Source 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.

In order to connect with SAP Ariba Source, set the following:

• API: Specify which API you would like the provider to retrieve SAP Ariba data from. Select the Supplier, Sourcing Project Management, or Contract API based on your business role (possible values are SupplierDataAPIWithPaginationV4, SourcingProjectManagementAPIV2, or ContractAPIV1).
• DataCenter: The data center where your account's data is hosted.
• Realm: The name of the site you want to access.
• Environment: Indicate whether you are connecting to a test or production environment (possible values are TEST or PRODUCTION).

If you are connecting to the Supplier Data API or the Contract API, additionally set the following:

• User: Id of the user on whose behalf API calls are invoked.
• PasswordAdapter: The password associated with the authenticating User.

If you're connecting to the Supplier API, set ProjectId to the Id of the sourcing project you want to retrieve data from.

Authenticating with OAuth

After setting connection properties, you need to configure OAuth connectivity to authenticate.

• Set AuthScheme to OAuthClient.
• Register an application with the service to obtain the APIKey, OAuthClientId and OAuthClientSecret.

  For more information on creating an OAuth application, refer to the Help documentation.

Automatic OAuth

After setting the following, you are ready to connect:

APIKey: The Application key in your app settings. OAuthClientId: The OAuth Client Id in your app settings. OAuthClientSecret: The OAuth Secret in your app settings.

When you connect, the provider automatically completes the OAuth process:

1. The provider obtains an access token from SAP Ariba and uses it to request data.
2. The provider refreshes the access token automatically when it expires.
3. The OAuth values are saved in memory relative to the location specified in OAuthSettingsLocation.

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

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

cnxn = mod.connect("API=SupplierDataAPIWithPagination-V4;APIKey=wWVLn7WTAXrIRMAzZ6VnuEj7Ekot5jnU;Environment=SANDBOX;Realm=testRealm;AuthScheme=OAuthClient;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query SAP Ariba Source

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

sql = "SELECT SMVendorID, Category FROM Vendors WHERE Region = 'USA'"

Extract, Transform, and Load the SAP Ariba Source Data

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

Loading SAP Ariba Source Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to SAP Ariba Source

table1 = [ ['SMVendorID','Category'], ['NewSMVendorID1','NewCategory1'], ['NewSMVendorID2','NewCategory2'], ['NewSMVendorID3','NewCategory3'] ]

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

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

cnxn = mod.connect("API=SupplierDataAPIWithPagination-V4;APIKey=wWVLn7WTAXrIRMAzZ6VnuEj7Ekot5jnU;Environment=SANDBOX;Realm=testRealm;AuthScheme=OAuthClient;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT SMVendorID, Category FROM Vendors WHERE Region = 'USA'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['SMVendorID','Category'], ['NewSMVendorID1','NewCategory1'], ['NewSMVendorID2','NewCategory2'], ['NewSMVendorID3','NewCategory3'] ]

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