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Python Connector Libraries for Zendesk Data Connectivity. Integrate Zendesk with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Zendesk Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Zendesk data.

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

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Zendesk data in Python. When you issue complex SQL queries from Zendesk, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Zendesk and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Zendesk Data

Connecting to Zendesk 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.

Connecting to Zendesk

To connect, set the URL and provide authentication. The URL is your Zendesk Support URL: https://{subdomain}.zendesk.com.

Authenticating to Zendesk

You can authenticate using the Basic or OAuth methods.

Using Basic Authentication

To use Basic authentication, specify your email address and password or your email address and an API token. Set User to your email address and follow the steps below to provide the Password or ApiToken.

  • Enable password access in the Zendesk Support admin interface at Admin > Channels > API.
  • Manage API tokens in the Zendesk Support Admin interface at Admin > Channels > API. More than one token can be active at the same time. Deleting a token deactivates it permanently.

Using OAuth Authentication

See the Getting Started guide in the CData driver documentation for an authentication guide.

Follow the procedure below to install SQLAlchemy and start accessing Zendesk 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 Zendesk Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Zendesk 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("zendesk:///?URL=https://[email protected]&Password=test123&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Zendesk 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 Tickets 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 Tickets(base): __tablename__ = "Tickets" Id = Column(String,primary_key=True) Subject = Column(String) ...

Query Zendesk 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("zendesk:///?URL=https://[email protected]&Password=test123&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Tickets).filter_by(Industry="Floppy Disks"): print("Id: ", instance.Id) print("Subject: ", instance.Subject) 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

Tickets_table = Tickets.metadata.tables["Tickets"] for instance in session.execute(Tickets_table.select().where(Tickets_table.c.Industry == "Floppy Disks")): print("Id: ", instance.Id) print("Subject: ", instance.Subject) print("---------")

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

Insert Zendesk Data

To insert Zendesk data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Zendesk.

new_rec = Tickets(Id="placeholder", Industry="Floppy Disks") session.add(new_rec) session.commit()

Update Zendesk Data

To update Zendesk data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Zendesk.

updated_rec = session.query(Tickets).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Industry = "Floppy Disks" session.commit()

Delete Zendesk Data

To delete Zendesk data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Tickets).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Zendesk to start building Python apps and scripts with connectivity to Zendesk data. Reach out to our Support Team if you have any questions.