Zendesk Chat to BigQuery

This page provides you with instructions on how to extract data from Zendesk Chat and load it into Google BigQuery. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

About Zendesk Chat

Zendesk Chat is a real-time online chat application that businesses can use to engage with customers. It was originally marketed as Zopim, but Zendesk acquired the company that developed it in 2014, integrated it with Zendesk, and renamed it Zendesk Chat in 2016.

What is Google BigQuery?

Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With all of that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.

Getting data out of Zendesk Chat

Zendesk Chat provides a REST API that lets you get information about accounts, agents, roles, and other elements, all of which have different syntax and return JSON objects with different attributes. If, for example, you wanted to retrieve a list of agents, you would call GET /api/v2/agents. This call has a couple of optional parameters that let you specify a range of agent IDs.

Sample Zendesk Chat data

The Zendesk Chat API returns data in JSON format. For example, the result of a call to retrieve agents might look like this:


[
  {
    "id" : 5,
    "first_name" : "John",
    "last_name" : "Doe",
    "display_name" : "Johnny",
    "create_date" : "2014-09-30T08:25:09Z",
    "email" : "johndoe@gmail.com",
    "roles" : {
      "owner": false,
      "administrator": false
    },
    "role_id": 3,
    "enabled" : 1,
    "departments" : []
  },
  {
    "id" : 8,
    "first_name" : "Kevin",
    "last_name" : "Doe",
    ...
  }
]

Preparing Zendesk Chat 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. The source API 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.

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Keeping Zendesk Chat 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. Now you can set up a cron job or continuous loop to keep pulling new data as it appears. But as with any code, once you write it, you have to maintain it. If Zendesk modifies its API, or 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.

Other data warehouse options

BigQuery is really great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Postgres or Redshift, which are two RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading this data into Postgres or Redshift, check out To Redshift and To Postgres.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Zendesk Chat data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.