How to use SQLAlchemy ORM to access Zuora Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Zuora and the SQLAlchemy toolkit, you can build Zuora-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Zuora data to query 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 CData Connector 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.

Follow the procedure below to install SQLAlchemy and start accessing Zuora through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Zuora Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Zuora data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("zuora:///?OAuthClientID=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&Tenant=USProduction&ZuoraService=DataQuery&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Zuora Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Invoices table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Invoices(base):
	__tablename__ = "Invoices"
	Id = Column(String,primary_key=True)
	BillingCity = Column(String)
	...

Query Zuora Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("zuora:///?OAuthClientID=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&Tenant=USProduction&ZuoraService=DataQuery&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Invoices).filter_by(BillingState="CA"):
	print("Id: ", instance.Id)
	print("BillingCity: ", instance.BillingCity)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Invoices_table = Invoices.metadata.tables["Invoices"]
for instance in session.execute(Invoices_table.select().where(Invoices_table.c.BillingState == "CA")):
	print("Id: ", instance.Id)
	print("BillingCity: ", instance.BillingCity)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

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.