Extract, Transform, and Load Freshdesk Data in Python

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

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

Connecting to Freshdesk Data

Connecting to Freshdesk 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.

FreshDesk makes use of basic authentication. To connect to data, set the following connection properties:

• Domain: Set this to the domain associated with your FreshDesk account. For example, in your URL: https://my_domain.freshdesk.com.
• APIKey: Set this to the API key associated with your FreshDesk account. To retrieve your API key, Log into your support Portal -> Click on profile picture in the top right corner -> profile settings page. The API key will be available below the change password section to the right.

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

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

cnxn = mod.connect("Domain=MyDomain;APIKey=myAPIKey;")

Create a SQL Statement to Query Freshdesk

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

sql = "SELECT Id, Name FROM Tickets WHERE Status = '2'"

Extract, Transform, and Load the Freshdesk Data

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

Loading Freshdesk Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Freshdesk

table1 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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

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

cnxn = mod.connect("Domain=MyDomain;APIKey=myAPIKey;")

sql = "SELECT Id, Name FROM Tickets WHERE Status = '2'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','Name'], ['NewId1','NewName1'], ['NewId2','NewName2'], ['NewId3','NewName3'] ]

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