Extract, Transform, and Load Zendesk Data in Python

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Zendesk Python Connector

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



The CData Python Connector for Zendesk enables you to create ETL applications and pipelines for Zendesk data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Zendesk and the petl framework, you can build Zendesk-connected applications and pipelines for extracting, transforming, and loading Zendesk data. This article shows how to connect to Zendesk with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

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

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

cnxn = mod.connect("URL=https://subdomain.zendesk.com;User=my@email.com;Password=test123;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Zendesk

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

sql = "SELECT Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'"

Extract, Transform, and Load the Zendesk Data

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

Loading Zendesk Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Tickets table.

Adding New Rows to Zendesk

table1 = [ ['Id','Subject'], ['NewId1','NewSubject1'], ['NewId2','NewSubject2'], ['NewId3','NewSubject3'] ]

etl.appenddb(table1, cnxn, 'Tickets')

With the CData Python Connector for Zendesk, you can work with Zendesk 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 Zendesk Python Connector to start building Python apps and scripts with connectivity to Zendesk data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.zendesk as mod

cnxn = mod.connect("URL=https://subdomain.zendesk.com;User=my@email.com;Password=test123;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Subject'], ['NewId1','NewSubject1'], ['NewId2','NewSubject2'], ['NewId3','NewSubject3'] ]

etl.appenddb(table3, cnxn, 'Tickets')