Freshdesk to Google Data Studio

This page provides you with instructions on how to extract data from Freshdesk and analyze it in Google Data Studio. (If the mechanics of extracting data from Freshdesk seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Freshdesk?

Freshdesk provides cloud-based customer support software for help desk staff.

Getting data out of Freshdesk

Freshdesk provides a REST API that lets you get data on tickets, agents, companies, and other information out of the service's back end. Some of the API calls are simple; for example, to list all tickets, you could call GET /api/v2/tickets. You can use optional filters (such as company ID and updated since date/time) in the GET request to limit the data you retrieve, and the optional include parameter to retrieve fields that the API doesn't send by default.

Sample Freshdesk data

Freshdesk returns information in JSON format. Each JSON object may contain more than a dozen attributes, which you have to parse before loading the data into your data warehouse. Here's an example of what some of the data for that call to return all tickets might look like:

[
  {
    "cc_emails" : ["user@cc.com", "user2@cc.com"],
    "fwd_emails" : [ ],
    "reply_cc_emails" : ["user@cc.com", "user2@cc.com"],
    "fr_escalated" : false,
    "spam" : false,
    "email_config_id" : null,
    "group_id" : 2,
    "priority" : 1,
    "requester_id" : 5,
    "responder_id" : 1,
    "source" : 2,
    "status" : 2,
    "subject" : "Please help",
    "to_emails" : null,
    "product_id" : null,
    "id" : 18,
    "type" : Lead,
    "created_at" : "2017-11-17T12:02:50Z",
    "updated_at" : "2017-11-17T12:02:51Z",
    "due_by" : "2017-11-20T11:30:00Z",
    "fr_due_by" : "2017-11-18T11:30:00Z",
    "is_escalated" : false,
    "description_text" : "Computer is not working as expected",
    "description" : "Computer is not working as expected",
    "custom_fields" : {
      "category" : "Default"
    }
  },
  {
    "cc_emails" : [ ],
    "fwd_emails" : [ ],
    "reply_cc_emails" : [ ],
    "fr_escalated" : false,
    "spam" : false,
    "email_config_id" : null,
    "group_id" : null,
    "priority" : 1,
    "requester_id" : 1,
    "responder_id" : null,
    "source" : 2,
    "status" : 2,
    "subject" : "",
    "to_emails" : null,
    "product_id" : null,
    "id" : 17,
    "type" : null,
    "created_at" : "2017-11-17T12:02:06Z",
    "updated_at" : "2017-11-17T12:02:07Z",
    "due_by" : "2017-11-20T11:30:00Z",
    "fr_due_by" : "2017-11-18T11:30:00Z",
    "is_escalated" : false,
    "description_text" : "Not given.",
    "description" : "
Not given.
", "custom_fields" : { "category" : null } } ]

Preparing Freshdesk data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Freshdesk's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Keeping Freshdesk data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Freshdesk.

And remember, as with any code, once you write it, you have to maintain it. If Freshdesk modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Freshdesk to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Freshdesk data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Freshdesk to Redshift, Freshdesk to BigQuery, and Freshdesk to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Freshdesk data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.