Freshdesk provides cloud-based customer support software for help desk staff.

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

Freshdesk provides a REST API that lets you get data on tickets, agents, companies, and other information out of the service's back end. Some of the API calls are simple; for example, to list all tickets, you could call GET /api/v2/tickets. You can use optional filters (such as company ID and updated since date/time) in the GET request to limit the data you retrieve, and the optional include parameter to retrieve fields that the API doesn't send by default.

Freshdesk returns information in JSON format. Each JSON object may contain more than a dozen attributes, which you have to parse before loading the data into your data warehouse. Here's an example of what some of the data for that call to return all tickets might look like:

[
  {
    "cc_emails" : ["user@cc.com", "user2@cc.com"],
    "fwd_emails" : [ ],
    "reply_cc_emails" : ["user@cc.com", "user2@cc.com"],
    "fr_escalated" : false,
    "spam" : false,
    "email_config_id" : null,
    "group_id" : 2,
    "priority" : 1,
    "requester_id" : 5,
    "responder_id" : 1,
    "source" : 2,
    "status" : 2,
    "subject" : "Please help",
    "to_emails" : null,
    "product_id" : null,
    "id" : 18,
    "type" : Lead,
    "created_at" : "2017-11-17T12:02:50Z",
    "updated_at" : "2017-11-17T12:02:51Z",
    "due_by" : "2017-11-20T11:30:00Z",
    "fr_due_by" : "2017-11-18T11:30:00Z",
    "is_escalated" : false,
    "description_text" : "Computer is not working as expected",
    "description" : "Computer is not working as expected",
    "custom_fields" : {
      "category" : "Default"
    }
  },
  {
    "cc_emails" : [ ],
    "fwd_emails" : [ ],
    "reply_cc_emails" : [ ],
    "fr_escalated" : false,
    "spam" : false,
    "email_config_id" : null,
    "group_id" : null,
    "priority" : 1,
    "requester_id" : 1,
    "responder_id" : null,
    "source" : 2,
    "status" : 2,
    "subject" : "",
    "to_emails" : null,
    "product_id" : null,
    "id" : 17,
    "type" : null,
    "created_at" : "2017-11-17T12:02:06Z",
    "updated_at" : "2017-11-17T12:02:07Z",
    "due_by" : "2017-11-20T11:30:00Z",
    "fr_due_by" : "2017-11-18T11:30:00Z",
    "is_escalated" : false,
    "description_text" : "Not given.",
    "description" : "
Not given.
",
    "custom_fields" : {
      "category" : null
    }
  }
]

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. Freshdesk's 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.

Once you've identified the columns you want to insert, you can use Redshift's CREATE TABLE statement to define a table to receive all of the data.

With a table built, you might be tempted to migrate your data (especially if there isn't much of it) by using INSERT statements to add data to your Redshift table row by row. Not so fast! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and use the COPY command to load it into Redshift.

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 Freshdesk.

And remember, as with any code, once you write it, you have to maintain it. If Freshdesk 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.

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, 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 BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.