Zendesk Chat is a real-time online chat application that businesses can use to engage with customers. It was originally marketed as Zopim. Zendesk acquired the company that developed it in 2014, integrated it with Zendesk, and renamed it Zendesk Chat in 2016.

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture, and separates compute and storage resources, enabling users to scale the two independently and pay only for resources used. It can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

Zendesk Chat provides a REST API that lets you get information about accounts, agents, roles, and other elements, all of which have different syntax and return JSON objects with different attributes. If, for example, you wanted to retrieve a list of agents, you would call GET /api/v2/agents. This call has a couple of optional parameters that let you specify a range of agent IDs.

The Zendesk Chat API returns data in JSON format. For example, the result of a call to retrieve agents might look like this:

[
  {
    "id" : 5,
    "first_name" : "John",
    "last_name" : "Doe",
    "display_name" : "Johnny",
    "create_date" : "2017-09-30T08:25:09Z",
    "email" : "johndoe@gmail.com",
    "roles" : {
      "owner": false,
      "administrator": false
    },
    "role_id": 3,
    "enabled" : 1,
    "departments" : []
  },
  {
    "id" : 8,
    "first_name" : "Kevin",
    "last_name" : "Doe",
    ...
  }
]

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Zendesk Chat documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

You may need to prepare your data before loading it. Check Snowflake's supported data types and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Turn to Snowflake's Data Loading Overview for help with the task of loading your data. If you're not loading a lot of data, you might be able to use Snowflake's data loading wizard, but its limitations make it unsuitable as a reliable ETL solution for some use cases. As an alternative, you can:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You’ll have the option of copying from your local drive or from Amazon S3 – and Snowflake lets you make a virtual warehouse to power the insertion process.

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Zendesk Chat.

And remember, as with any code, once you write it, you have to maintain it. If Zendesk modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Snowflake 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, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. 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 Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.