How to integrate Zuora with Apache Airflow

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

Configuring the Connection to Zuora

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Zuora 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.

Zuora uses the OAuth standard to authenticate users. See the online Help documentation for a full OAuth authentication guide.

Configuring Tenant property

In order to create a valid connection with the provider you need to choose one of the Tenant values (USProduction by default) which matches your account configuration. The following is a list with the available options:

• USProduction: Requests sent to https://rest.zuora.com.
• USAPISandbox: Requests sent to https://rest.apisandbox.zuora.com"
• USPerformanceTest: Requests sent to https://rest.pt1.zuora.com"
• EUProduction: Requests sent to https://rest.eu.zuora.com"
• EUSandbox: Requests sent to https://rest.sandbox.eu.zuora.com"

Selecting a Zuora Service

Two Zuora services are available: Data Query and AQuA API. By default ZuoraService is set to AQuADataExport.

DataQuery

The Data Query feature enables you to export data from your Zuora tenant by performing asynchronous, read-only SQL queries. We recommend to use this service for quick lightweight SQL queries.

• The maximum number of input records per table after filters have been applied: 1,000,000
• The maximum number of output records: 100,000
• The maximum number of simultaneous queries submitted for execution per tenant: 5
• The maximum number of queued queries submitted for execution after reaching the limitation of simultaneous queries per tenant: 10
• The maximum processing time for each query in hours: 1
• The maximum size of memory allocated to each query in GB: 2
• The maximum number of indices when using Index Join, in other words, the maximum number of records being returned by the left table based on the unique value used in the WHERE clause when using Index Join: 20,000

AQuADataExport

AQuA API export is designed to export all the records for all the objects ( tables ). AQuA query jobs have the following limitations:

• If a query in an AQuA job is executed longer than 8 hours, this job will be killed automatically.
• The killed AQuA job can be retried three times before returned as failed.

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.: zuora_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:zuora:RTK=5246...;OAuthClientID=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;Tenant=USProduction;ZuoraService=DataQuery;InitiateOAuth=GETANDREFRESH)
   • Driver Class: cdata.jdbc.zuora.ZuoraDriver
   • Driver Path: PATH/TO/cdata.jdbc.zuora.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 Zuora 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 zuora_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="zuora_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 "zuora_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 zuora_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 Zuora data is now available for use in CSV format thanks to Apache Airflow.

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