Zendesk to Postgres

This page provides you with instructions on how to extract data from Zendesk and load it into PostgreSQL. (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 Zendesk?

Zendesk is an online customer service and support ticketing (help desk) system.

What is PostgreSQL?

PostgreSQL, or Postgres, is a popular object-relational database management system (ORDBMS) that calls itself "the world's most advanced open source database." It offers enterprise-grade features, maintains a strong emphasis on extensibility, and is licensed as open source software.

PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's ACID-compliant and supports foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is frequently used as a back-end database for web systems, and is available in cloud-based deployments from most major cloud vendors. PostgreSQL's syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless and makes Postgres a good platform for developers who may eventually work on Amazon's data warehouse platform.

Getting data out of Zendesk

You can extract data from Zendesk's servers using the Zendesk REST API, which exposes data about tickets, agents, clients, groups, and more. To get data on a ticket, for example, you could call GET /api/v2/tickets.json.

Sample Zendesk data

The Zendesk API returns JSON-formatted data. Here's 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":       "2017-07-20T22:55:29Z",
  "updated_at":       "2017-08-05T10:38:52Z",
  "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 Postgres

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.

For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.

For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.

The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.

Keeping Zendesk up to date

You've built a script that pulls data from Zendesk and loads it into your destination database, but 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 identify new records. Once you've taken new data into account, you can set up your script as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

PostgreSQL is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, and To Panoply.

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 PostgreSQL data warehouse.