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

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Zuora data in Python. When you issue complex SQL queries from 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).

Connecting to Zuora Data

Connecting to Zuora 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.

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.

Limitations

• 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:

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.

After installing the CData Zuora Connector, follow the procedure below to install the other required modules and start accessing Zuora 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 Zuora 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.zuora as mod

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

cnxn = mod.connect("OAuthClientID=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;Tenant=USProduction;ZuoraService=DataQuery;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Zuora

Use SQL to create a statement for querying Zuora. In this article, we read data from the Invoices entity.

sql = "SELECT Id, BillingCity FROM Invoices WHERE BillingState = 'CA'"

Extract, Transform, and Load the Zuora Data

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

Loading Zuora Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Zuora, you can work with Zuora 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 Zuora to start building Python apps and scripts with connectivity to Zuora 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.zuora as mod

cnxn = mod.connect("OAuthClientID=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;Tenant=USProduction;ZuoraService=DataQuery;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, BillingCity FROM Invoices WHERE BillingState = 'CA'"

table1 = etl.fromdb(cnxn,sql)

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

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