Freshdesk to Snowflake

This page provides you with instructions on how to extract data from Freshdesk and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Freshdesk?

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

What is Snowflake?

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.

Getting data out of Freshdesk

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.

Sample Freshdesk data

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" : ["", ""],
    "fwd_emails" : [ ],
    "reply_cc_emails" : ["", ""],
    "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 } } ]

Preparing Freshdesk data

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.

Preparing data for Snowflake

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

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

Loading data into Snowflake

Snowflake's documentation outlines a Data Loading Overview that can lead you through the task of loading your data. If you're not loading a lot of data, Snowflake's data loading wizard may be helpful, but for many organizations, its limitations make it unacceptable. Instead, you can:

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

You can copy data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

Keeping Freshdesk data up to date

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.

Other data warehouse options

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.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Freshdesk to Snowflake automatically. With just a few clicks, Stitch starts extracting your Freshdesk data, structuring it in a way that's optimized for analysis, and inserting that data into your Snowflake data warehouse.