How to load data from Zendesk Chat to Google BigQuery

Access your data on Zendesk Chat

The first step in loading your Zendesk Chat data to any kind of data warehouse solution is to access your data and start extracting it.

Zendesk Chat offers a rich and well-defined API that belongs to the Representational State Transfer (REST) category. Using it you can perform RESTful operations such as reading, modifying, adding, and deleting your helpdesk data, thus allowing you to programmatically interact with your account.

Among the 10 provided resources, you can find information about Accounts, Agents, Visitors, Chats, Shortcuts, Triggers, Bans, Departments, Goals, Skills, and Roles.

In addition to the above, the things that you have to keep in mind when dealing with the Zendesk Chat API, are:

• Rate limits. The API is rate limited, i.e. it only allows a certain number of requests per minute.
• Authentication. If the Zendesk Chat account is created in Zendesk Support, the user must authenticate with an OAuth access token.
  If a stand-alone Chat account is used then either a basic authentication can be used or an OAuth access token.
• Paging and dealing with a big amount of data.

Transform and prepare your Zendesk Chat Data

After you have accessed your data on Zendesk Chat, you will have to transform it based on two main factors,

1. The limitations of the database that the data will be loaded onto
2. The type of analysis that you plan to perform

Each system has specific limitations on the data types and data structures that it supports. If for example, you want to push data into Google BigQuery, then you can send nested data like JSON directly.

Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading it into the database.

Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. Zendesk Chat has a very limited set of available data types which means that your work to do these mappings is much easier and straightforward, but nonetheless equally important with any other case of a data source.

Due to the rich and complex data model that Zendesk Chat follows, some of the provided resources might have to be flattened out and be pushed in more than one table.

Load data from Zendesk Chat to Google BigQuery

If you want data to be loaded from Zendesk Chat to 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 must load your own data to 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 do not forget to follow the best practices.

Another option is to post data through the JSON API, as we see again APIs play an important role in both the extraction but also the loading of data to our data warehouse. In its simplest case, it’s just a matter of one HTTP POST request using a tool like CURL or Postman.

After you have loaded your data in Google Cloud Storage, you have to create a Load Job for BigQuery to actually load any 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 best way to load data from Zendesk Chat to BigQuery

So far we just scraped the surface of what you can do with BigQuery and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

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