Zendesk to Azure SQL Data Warehouse

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

Azure SQL Data Warehouse is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.

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 Azure SQL Data Warehouse

SQL Data Warehouse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:

  • AZCopy uses the public internet.
  • Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
  • The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.

From Azure Storage you can load the data into SQL Data Warehouse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.

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

Azure SQL Data Warehouse 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, PostgreSQL, Snowflake, or Panoply, which are RDBMSes that use similar SQL syntax. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, 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 move data from Zendesk to Azure SQL Data Warehouse 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 Azure SQL Data Warehouse data warehouse.