How to load data from Zendesk to SQL Data Warehouse

Blendo Team

How may I load data from Zendesk to SQL Data Warehouse for further analysis? The purpose of this post is to help you define a process or pipeline, for getting your subscription related data from Zendesk and load it into SQL Data Warehouse for further analysis. We will see how to access and extract your data from Zendesk through its API and how to load it into SQL Data Warehouse. This process requires from you to write the code to get the data and make sure that this process will run every time new data are generated. Alternatively you can use products like Blendo that can handle this kind of problems automatically for you.

About Zendesk

Import your Zendesk data into your data warehouse -

Load data from Zendesk

Zendesk provides a cloud-based customer service platform, that includes ticketing, self-service options, and customer support features. Zendesk focuses on improving the communication between your customers and your company. It brings all your customer communication into one place. The supported communication channels are:

  • Mail. Zendesk helps to organise all the emails you receive from your customers.

  • Social. You can connect your Facebook and Twitter accounts with Zendesk.

  • Voice. Take customer calls from within Zendesk.

  • Chat. Zopim Chat allows you to communicate with your customers from within your product.

Zendesk’s help desk software helps streamline customer support with time-saving tools like triggers and automations. And it’s intuitive, built with the experience of customer service and support desk agents in mind. Some important features of the ticketing system that Zendesk offers, are:

  • It helps to Solve tickets better with teammates through a collaborative environment where information can be shared.

  • Get access to relevant information across teams. Zendesk comes with an internal Knowledge Base that allows agents to quickly refer to information and processes without losing their place.

  • Make everyone inside your company a support agent. With light agents, everyone inside your company can view tickets and make private comments.

Additionally, Zendesk offers a suite of analytics tools that will help you to get closer to your user through data. With these tools you can:

  • Gain visibility into customers interactions

  • Measure your team’s performance

  • See the business impacts of great service

It is possible to track a large number of metrics related to your customers, support teams and your business.

But in case that you would like to run some more engaged analysis with your Zendesk data, or fuse the customer support related data with data from other sources like your transactional database and logs, Zendesk exposes a rich ecosystem of APIs and tools that you can use to access and pull your data among other functionalities.

About Microsoft Azure SQL Data Warehouse

SQL Data Warehouse is the data warehousing solution that you can use if you are a user of Microsoft Azure. It’s an elastic data warehousing as a service solution, emphasising it’s enterprise focus. It also speaks SQL like the previous two solutions and it supports querying both relational and non-relational data.  It offers a number of enterprise-class features like support for hybrid cloud installations and strong security. It’s probably the less mature solution compared to the two others though, it’s still in “Preview” mode although accessible to existing Azure subscribers.

Extract your data from Zendesk

Zendesk APIs are not specific to pulling data, Zendesk provides more than a hundred different APIs for you to integrate with. So you can easily manage your users, enhance your team’s productivity and create seamless integrations. You can create integrations or even enrich Zendesk with data from external sources. Zendesk API is a RESTful API that can be access through HTTP. As a RESTful API, interacting with it an be achieved by using tools like CURL or Postman or by using http clients for your favourite language or framework.

A few suggestions:

Additionally, Zendesk offers a number of SDKs and libraries so you can access the API from your framework of choice without having to deal with the technicalities of HTTP. API clients are available for the following languages:

Zendesk API Authentication

Zendesk’s API is an SSL-only API, regardless of how your account is configured. You must be a verified user to make API requests. You can authorize against the API using either basic authentication with your email address and password, with your email address and an API token, or with an OAuth access token.

Zendesk rate limiting

The API is rate limited. It only allows a certain number of requests per minute depending on your plan and the endpoint. Zendesk reserves the right to adjust the rate limit for given endpoints to provide a high quality of service for all clients. The current limits are the following:

Plan Requests per minute
Essential 10
Team 200
Professional 400
Enterprise 700
High Volume API Add-On (Professional or Enterprise) 2500



By default, most list endpoints return a maximum of 100 records per page. You can change the number of records on a per-request basis by passing a per_page parameter in the request URL parameters. Example: per_page=50. However, you can’t exceed 100 records per page on most endpoints.

When the response exceeds the per-page maximum, you can paginate through the records by incrementing the page parameter. Example: page=3. List results include next_page and previous_page URLs in the response body for easier navigation:

  "users": [ ... ],
  "count": 1234,
  "next_page": "",
  "previous_page": null

Endpoints and available resources

The Zendesk REST API exposes a large number of resources and endpoints that allows the user to interact with the platform in every possible way. Thus it is possible to create new applications on top of the Zendesk platform, integrate external systems with it and of course pull data out of the platform. The most important resources are the following:

  • The tickets that your customers create through Zendesk.
  • Ticket events. Changes that have occurred to the tickets.
  • Organizations.
  • Users.
  • Ticket metrics. These are metrics related to your tickets.
  • Data related to the Net Promoter Score.
  • Articles

Let’s assume that we want to pull all the tickets we have on Zendesk. To do that we need to perform a GET request to the appropriate end-point, like this: GET /api/v2/incremental/tickets.json?start_time=1332034771

curl https://{subdomain} \
  -v -u {email_address}:{password}

And a sample response:

Status: 200 OK

  "end_time": 1383685952,
  "next_page": "https://{subdomain}",
  "count": 1,
  "tickets": [
      "url": "https://{subdomain}",
      "id": 2,
      "created_at": "2012-02-02T04:31:29Z",
      "generated_timestamp": 1390362285

A complete ticket object might contain the following fields:

ame Type Read-only Mandatory Comment
id integer yes no Automatically assigned when creating tickets
url string yes no The API url of this ticket
external_id string no no An id you can use to link Zendesk tickets to local records
type string no no The type of this ticket, i.e. “problem”, “incident”, “question” or “task”
subject string no no The value of the subject field for this ticket
raw_subject string no no The dynamic content placeholder, if present, or the “subject” value, if not. See Dynamic Content
description string yes no The first comment on the ticket
priority string no no Priority, defines the urgency with which the ticket should be addressed: “urgent”, “high”, “normal”, “low”
status string no no The state of the ticket, “new”, “open”, “pending”, “hold”, “solved”, “closed”
recipient string no no The original recipient e-mail address of the ticket
requester_id integer no yes The user who requested this ticket
submitter_id integer no no The user who submitted the ticket; The submitter always becomes the author of the first comment on the ticket.
assignee_id integer no no What agent is currently assigned to the ticket
organization_id integer yes no The organization of the requester
group_id integer no no The group this ticket is assigned to
collaborator_ids array no no Who are currently CC’ed on the ticket
forum_topic_id integer no no The topic this ticket originated from, if any
problem_id integer no no The problem this incident is linked to, if any
has_incidents boolean yes no Is true of this ticket has been marked as a problem, false otherwise
due_at date no no If this is a ticket of type “task” it has a due date. Due date format uses ISO 8601 format.
tags array no no The array of tags applied to this ticket
via Via yes no This object explains how the ticket was created
custom_fields array no no The custom fields of the ticket
satisfaction_rating object yes no The satisfaction rating of the ticket, if it exists, or the state of satisfaction, ‘offered’ or ‘unoffered’
sharing_agreement_ids array yes no The ids of the sharing agreements used for this ticket
followup_ids array yes no The ids of the followups created from this ticket – only applicable for closed tickets
ticket_form_id integer no no The id of the ticket form to render for this ticket – only applicable for enterprise accounts
brand_id integer no no The id of the brand this ticket is associated with – only applicable for enterprise accounts
created_at date yes no When this record was created
updated_at date yes no When this record last got updated

The results of the Zendesk API is always in JSON format. The API offers you the opportunity to get very granular data about your accounting activities and use it for analytic and reporting purposes.

Load Data from Zendesk to SQL Data Warehouse

SQL Data Warehouse support numerous options for loading data, such us:

  • PolyBase
  • Azure Data Factory
  • BCP command-line utility
  • SQL Server integration services

As we are interested in loading data from online services by using their exposed HTTP APIs, we are not going to consider the usage of BCP command-line utility or SQL server integration in this guide. We’ll consider the case of loading our data as Azure storage Blobs and then use PolyBase to load the data into SQL Data Wareho use.

Accessing these services happens through HTTP APIs, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. You can access these APIs by using a tool like CURL or Postman. Or use the libraries provided by Microsoft for your favourite language. Before you actually upload any data you have to create a container which is something similar as a concept to the Amazon AWS Bucket, creating a container is a straight forward operation and you can do it by following the instructions found on the Blog storage documentation from Microsoft. As an example, the following code can create a container in Node.js.

blobSvc.createContainerIfNotExists('mycontainer', function(error, result, response){
    // Container exists and allows
    // anonymous read access to blob
    // content and metadata within this container

After the creation of the container you can start uploading data to it by using again the given SDK of your choice in a similar fashion:

blobSvc.createBlockBlobFromLocalFile('mycontainer', 'myblob', 'test.txt', function(error, result, response){
    // file uploaded

When you are done putting your data into Azure Blobs you are ready to load it into SQL Data Warehouse using PolyBase. To do that you should follow the directions in the Load with PolyBase documentation. In a summary the required steps to do it, are the following:

  • create a database master key
  • create a database scoped credentials
  • create an external file format
  • create an external data source

PolyBase’s ability to transparently parallelize loads from Azure Blob Storage will make it the fastest tool for loading data. After configuring PolyBase, you can load data directly into your SQL Data Warehouse by simply creating an external table that points to your data in storage and then mapping that data to a new table within SQL Data Warehouse.

Of course you will need to establish a recurrent process that will extract any newly created data from your service, load them in the form of Azure Blobs and initiate the PolyBase process for importing the data again into SQL Data Warehouse. One way of doing this is by using the Azure Data Factory service. In case you would like to follow this path you can read some good documentation on how to move data to and from Azure SQL Warehouse using Azure Data Factory.


The best way to load data from Zendesk to SQL Data Warehouse and possible alternatives

So far we just scraped the surface of what can be done with Microsoft Azure SQL Data Warehouse and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources. A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like Blendo that can handle this kind of problems automatically for you.

 Blendo integrates with multiple sources or services like databases, CRM, email campaigns, analytics and more.  Quickly and safely move all your data from Recurly into SQL Data Warehouse and start generating insights from your data.