Zendesk to BigQuery

This page provides you with instructions on how to extract data from Zendesk 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.)

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

In order to get your Zendesk data into your data warehouse, you have to start by extracting it from Zendesk’s servers. You can do this using the Zendesk API. Zendesk allows access to the API for all of its customers. The API documentation can be accessed at this link.

Data from the Zendesk API can be retrieved programmatically via REST requests. The API gives you access to plenty of data. Tickets, agents, clients, and groups are some examples. Use the API docs to retrieve the data you'd like to load into your warehouse.

Sample Zendesk data

The Zendesk API returns JSON-formatted data. Below is an example of the kind of response you might see when querying for the details of a ticket.

{
  "id":               35436,
  "url":              "https://company.zendesk.com/api/v2/tickets/35436.json",
  "external_id":      "ahg35h3jh",
  "created_at":       "2009-07-20T22:55:29Z",
  "updated_at":       "2011-05-05T10:38:52Z",
  "type":             "incident",
  "subject":          "Help, my printer is on fire!",
  "raw_subject":      "{{dc.printer_on_fire}}",
  "description":      "The fire is very colorful.",
  "priority":         "high",
  "status":           "open",
  "recipient":        "support@company.com",
  "requester_id":     20978392,
  "submitter_id":     76872,
  "assignee_id":      235323,
  "organization_id":  509974,
  "group_id":         98738,
  "collaborator_ids": [35334, 234],
  "forum_topic_id":   72648221,
  "problem_id":       9873764,
  "has_incidents":    false,
  "due_at":           null,
  "tags":             ["enterprise", "other_tag"],
  "via": {
    "channel": "web"
  },
  "custom_fields": [
    {
      "id":    27642,
      "value": "745"
    },
    {
      "id":    27648,
      "value": "yes"
    }
  ],
  "satisfaction_rating": {
    "id": 1234,
    "score": "good",
    "comment": "Great support!"
  },
  "sharing_agreement_ids": [84432]
}

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 up to date

So, now what? You’ve built a script that pulls data from Zendesk and loads it into your destination database. What happens tomorrow when you have dozens of new tickets and related data?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Zendesk’s API returns updated_at fields that allow you to quickly identify new records. The final step is to set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

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 data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.