How to integrate SAP Ariba Source with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for SAP Ariba Source, Airflow can work with live SAP Ariba Source data. This article describes how to connect to and query SAP Ariba Source data from an Apache Airflow instance and store the results in a CSV file.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live SAP Ariba Source data. When you issue complex SQL queries to 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). Its built-in dynamic metadata querying allows you to work with and analyze SAP Ariba Source data using native data types.

Configuring the Connection to SAP Ariba Source

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SAP Ariba Source JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

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.

To host the JDBC driver in clustered environments or in the cloud, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

The following are essential properties needed for our JDBC connection.

Establishing a JDBC Connection within Airflow

1. Log into your Apache Airflow instance.
2. On the navbar of your Airflow instance, hover over Admin and then click Connections.
3. Next, click the + sign on the following screen to create a new connection.
4. In the Add Connection form, fill out the required connection properties:
   • Connection Id: Name the connection, i.e.: saparibasource_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:saparibasource:RTK=5246...;API=SupplierDataAPIWithPagination-V4;APIKey=wWVLn7WTAXrIRMAzZ6VnuEj7Ekot5jnU;Environment=SANDBOX;Realm=testRealm;AuthScheme=OAuthClient;InitiateOAuth=GETANDREFRESH)
   • Driver Class: cdata.jdbc.saparibasource.SAPAribaSourceDriver
   • Driver Path: PATH/TO/cdata.jdbc.saparibasource.jar
5. Test your new connection by clicking the Test button at the bottom of the form.
6. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections:

Creating a DAG

A DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow. Our workflow is to simply run a SQL query against SAP Ariba Source data and store the results in a CSV file.

1. To get started, in the Home directory, there should be an "airflow" folder. Within there, we can create a new directory and title it "dags". In here, we store Python files that convert into Airflow DAGs shown on the UI.
2. Next, create a new Python file and title it sap ariba source_hook.py. Insert the following code inside of this new file:

   	import time
   	from datetime import datetime
   	from airflow.decorators import dag, task
   	from airflow.providers.jdbc.hooks.jdbc import JdbcHook
   	import pandas as pd
   
   	# Declare Dag
   	@dag(dag_id="sap ariba source_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=['load_csv'])
   	
   	# Define Dag Function
   	def extract_and_load():
   	# Define tasks
   		@task()
   		def jdbc_extract():
   			try:
   				hook = JdbcHook(jdbc_conn_id="jdbc")
   				sql = """ select * from Account """
   				df = hook.get_pandas_df(sql)
   				df.to_csv("/{some_file_path}/{name_of_csv}.csv",header=False, index=False, quoting=1)
   				# print(df.head())
   				print(df)
   				tbl_dict = df.to_dict('dict')
   				return tbl_dict
   			except Exception as e:
   				print("Data extract error: " + str(e))
               
   		jdbc_extract()
       
   	sf_extract_and_load = extract_and_load()
   

3. Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "sap ariba source_hook".
4. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e. play) button to run the DAG. This executes the SQL query in our sap ariba source_hook.py file and export the results as a CSV to whichever file path we designated in our code.
5. After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv.
6. Open the CSV file to see that your SAP Ariba Source data is now available for use in CSV format thanks to Apache Airflow.

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