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By Rudderstack Team

How to load data from the Zendesk to Google BigQuery

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 Google BigQuery for further analysis. We will see how to access and extract data from Zendesk through its API and how to load it into Google BigQuery. This process requires you to write the code to get the data and make sure that this process will run every time new data is generated. Alternatively, you can use products like RudderStack that can handle this kind of problem automatically for you.


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 accessed through HTTP. As a RESTful API, interacting with it can be achieved by using tools like CURL or Postman or by using http clients for your favorite 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:

Pagination

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": "https://account.zendesk.com/api/v2/users.json?page=2",
"previous_page": null
}

Endpoints and available resources

The Zendesk REST API exposes a large number of resources and endpoints that allow 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}.zendesk.com/api/v2/incremental/tickets.json?start_time=1332034771 \
-v -u {email_address}:{password}

And a sample response:

Status: 200 OK
{
"end_time": 1383685952,
"next_page": "https://{subdomain}.zendesk.com/api/v2/incremental/tickets.json?start_time=1383685952",
"count": 1,
"tickets": [
{
"url": "https://{subdomain}.zendesk.com/api/v2/tickets/1.json",
"id": 2,
"created_at": "2012-02-02T04:31:29Z",
"generated_timestamp": 1390362285
...
},
...
]
}

A complete ticket object might contain the following fields:

The results of the Zendesk API are 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.

About 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 organize all the emails you receive from your customers.
  • Social. You can connect your Facebook and Twitter account 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 automation. 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 refer to information and processes without losing their place quickly.
  • 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.

Prepare your data to be sent from Zendesk to Google BigQuery

Before you load your data into BigQuery, you should make sure that it is presented in a format supported by it, so for example if the API you pull data from returns XML you have to first transform it into a serialization that BigQuery understands. Currently, two data formats are supported:

You also need to make sure that the data types you are using are the ones supported by BigQuery, which are the following:

  • STRING
  • INTEGER
  • FLOAT
  • BOOLEAN
  • RECORD
  • TIMESTAMP

For more information, please check the Preparing Data for BigQuery page on the documentation.

About Google BigQuery

BigQuery is the data warehousing solution of Google. It’s part of the Google Cloud Platform, and it also speaks SQL as Redshift does. Queries are executed against append-only tables using the processing power of Google’s infrastructure. It is also fully managed and is offered as a service over the cloud. You can interact with it through its web UI, using a command-line tool while a variety of client libraries exist, so you can interact with it through your application.

Load Data from Zendesk to Google BigQuery

If you want to load data from Zendesk to Google BigQuery, you have to use one of the following supported data sources.

  1. Google Cloud Storage
  2. Sent data directly to BigQuery with a POST request
  3. Google Cloud Datastore Backup
  4. Streaming insert
  5. App Engine log files
  6. Cloud Storage logs

From the above list of sources, 5 and 6 are not applicable in our case.

For Google Cloud Storage, you first have to load your data into it, there are a few options on how to do this, for example, you can use the console directly as it is described here and does not forget to follow the best practices. Another option is to post your data through the JSON API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. In its simplest case, it’s just a matter of one HTTP POST request using a tool like CURL or Postman. It should look like the following example.

POST /upload/storage/v1/b/myBucket/o?uploadType=media&name=myObject HTTP/1.1
Host: www.googleapis.com
Content-Type: application/text
Content-Length:
number_of_bytes_in_file
Authorization: Bearer
your_auth_token your Zendesk data
and if everything went ok, you should get something like the following as a response from the server:
HTTP/1.1 200
Content-Type: application/json
{
"name": "myObject"
}

Working with Curl or Postman, is good only for testing, if you would like to automate the process of loading your data into Google Bigquery, you should write some code to send your data to Google Cloud Storage. In case you are developing on the Google App Engine you can use the library that is available for the languages that are supported by it:

If you are using one of the above languages and you are not coding for the Google App Engine, you can use it to access the Cloud Storage from your environment. Interacting with such a feature-rich product like Google Cloud Storage can become quite complicated depending on your use case. For more details on the different options that exist, you can check Google Cloud Storage documentation. If you are looking for a less engaged and more neutral way of using Cloud Storage, you can consider a solution like RudderStack.

After you have loaded your data into Google Cloud Storage, you have to create a Load Job for BigQuery to actually load the data into it, this Job should point to the source data in Cloud Storage that have to be imported, this happens by providing source URIs that point to the appropriate objects.

The previous method described, used a POST request to the Google Cloud Storage API for storing the data there and then load it into BigQuery. Another way to go is to do a direct HTTP POST request to BigQuery with the data you would like to query. This approach is similar to how we loaded the data to Google Cloud Storage through the JSON API, but it uses the appropriate end-points of BigQuery to load the data there directly. The way to interact with it is quite similar, for more information can be found on the Google BigQuery API Reference and on the page that describes how to load data into BigQuery using POST. You can interact with it using the HTTP client library of the language or framework of your choice. A few options are:

The best way to load data from Zendesk to Google BigQuery and possible alternatives

So far we just scraped the surface of what can be done with Google BigQuery 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 your use case requirements. Things can get even more complicated if you want to integrate data coming from different sources. Instead of writing, hosting, and maintaining a flexible data infrastructure, a possible alternative is to use a product like RudderStack that can automatically handle this kind of problem for you.

RudderStack integrates with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely move all your data from Zendesk to Google BigQuery and start generating insights from your data.

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