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

PostgreSQL, sometimes referred to as Postgres, calls itself "the world's most advanced open source database." The popular object-relational database management system (ORDBMS) offers enterprise-grade features with a strong emphasis on extensibility and standards compliance.

PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's open source, ACID-compliant, and has full support for foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL is often employed as a back-end database for web systems and software tools. It's available in cloud-based deployments by most major cloud vendors. And since its syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, Postgres a good tool for developers who may later use Redshift's data warehouse platform.

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 have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.

For simple, day-to-day data insertion, running INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.

For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.

The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.

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.

PostgreSQL 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, Snowflake, 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 Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.