How to integrate Oracle Eloqua Reporting with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for Oracle Eloqua Reporting, Airflow can work with live Oracle Eloqua Reporting data. This article describes how to connect to and query Oracle Eloqua Reporting 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 Oracle Eloqua Reporting data. When you issue complex SQL queries to Oracle Eloqua Reporting, the driver pushes supported SQL operations, like filters and aggregations, directly to Oracle Eloqua Reporting 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 Oracle Eloqua Reporting data using native data types.

Configuring the Connection to Oracle Eloqua Reporting

Built-in Connection String Designer

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

java -jar cdata.jdbc.oracleeloquareporting.jar

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

Oracle Eloqua Reporting supports the following authentication methods:

• Basic authentication (User and Password)
• OAuth 2.0 code grant flow
• OAuth 2.0 password grant flow

Basic Authentication (User and Password)

To perform authentication with a user and password, specify these properties:

• AuthScheme: Basic.
• Company: The company name associated with your Oracle Eloqua Reporting account.
• User: Your login account name.
• Password: Your login password.

OAuth Authentication (Code Grant Flow)

To authenticate with the OAuth code grant flow, you must set AuthScheme to OAuth and create a custom OAuth application. For information about how to create a custom OAuth application, see the Help documentation.

Then set the following properties:

• InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
• OAuthClientId: The client Id assigned when you registered your application.
• OAuthClientSecret: The client secret that was assigned when you registered your application.
• CallbackURL: The redirect URI that was defined when you registered your application.

When you connect, the driver opens Oracle Eloqua Reporting's OAuth endpoint in your default browser. Log in and grant permissions to the application. When the access token expires, the driver refreshes it automatically.

OAuth Authentication (Password Grant Flow)

With the OAuth password grant flow, you can use your OAuth application's credentials alongside your user credentials to authenticate without the need to grant permission manually via a browser prompt. You must create an OAuth app (see the Help documentation) to use this authentication method.

Set the following properties:

• AuthScheme: OAuthPassword
• Company: The company's unique identifier.
• User: Your login account name.
• Password: Your login password.
• OAuthClientId: The client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.

👁 Using the built-in connection string designer to generate a JDBC URL (oracle eloqua reporting is shown.)

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. 👁 Clicking 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.: oracleeloquareporting_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:oracleeloquareporting:RTK=5246...;AuthScheme=Basic;User=user;Password=password;Company=MyCompany;)
   • Driver Class: cdata.jdbc.oracleeloquareporting.OracleEloquaReportingDriver
   • Driver Path: PATH/TO/cdata.jdbc.oracleeloquareporting.jar
   👁 Add JDBC connection form
   
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: 👁 New connection added
   

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 Oracle Eloqua Reporting 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 oracle eloqua reporting_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="oracle eloqua reporting_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 "oracle eloqua reporting_hook". 👁 New DAG added
   
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 oracle eloqua reporting_hook.py file and export the results as a CSV to whichever file path we designated in our code. 👁 Run the DAG
   
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. 👁 CSV created
   
6. Open the CSV file to see that your Oracle Eloqua Reporting data is now available for use in CSV format thanks to Apache Airflow. 👁 CSV file with Oracle Eloqua Reporting data.
   

More Information & Free Trial

Download a free, 30-day trial of the CData JDBC Driver for Oracle Eloqua Reporting and start working with your live Oracle Eloqua Reporting data in Apache Airflow. Reach out to our Support Team if you have any questions.