Desk.com to Superset

This page provides you with instructions on how to extract data from Desk.com and analyze it in Superset. (If the mechanics of extracting data from Desk.com seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Desk?

Desk.com, owned by Salesforce, is an online help desk and customer service application aimed at small businesses. It supports email, phone, and social media channels, and provides a reporting interface for tracking ticket and agent performance.

What is Superset?

Apache Superset is a cloud-native data exploration and visualization platform that businesses can use to create business intelligence reports and dashboards. It includes a state-of-the-art SQL IDE, and it's open source software, free of cost. The platform was originally developed at Airbnb and donated to the Apache Software Foundation.

Getting data out of Desk

Desk.com provides a REST API that lets you pull information on dozens of categories. If you wanted to get a list of topics in your system, for example, you could call GET /api/v2/topics. You can provide optional parameters to limit and sort the information returned.

Sample Desk data

Desk.com's API returns JSON-format data. The data returned for a "list topics" call might look like this:

{
  "total_entries": 2,
  "page": 1,
  "_links": {
    "self": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "first": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "last": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "next": null,
    "previous": null
  },
  "_embedded": {
    "entries": [
      {
        "name": "Customer Support",
        "description": "This is key to going from good to great",
        "position": 1,
        "allow_questions": true,
        "in_support_center": true,
        "created_at": "2017-10-08T18:18:06Z",
        "updated_at": "2017-10-13T18:18:06Z",
        "_links": {
          "self": {
            "href": "/api/v2/topics/1",
            "class": "topic"
          },
          "articles": {
            "href": "/api/v2/topics/1/articles",
            "class": "article"
          },
          "translations": {
            "href": "/api/v2/topics/1/translations",
            "class": "topic_translation"
          }
        }
      },
      {
        "name": "Another Topic",
        "description": "Not the first one, but another one!",
        "position": 2,
        "allow_questions": true,
        "in_support_center": true,
        "created_at": "2017-10-08T18:18:06Z",
        "updated_at": "2017-10-13T18:18:06Z",
        "_links": {
          "self": {
            "href": "/api/v2/topics/2",
            "class": "topic"
          },
          "articles": {
            "href": "/api/v2/topics/2/articles",
            "class": "article"
          },
          "translations": {
            "href": "/api/v2/topics/2/translations",
            "class": "topic_translation"
          }
        }
      }
    ]
  }
}

Preparing Desk 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. Desk.com'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.

Loading data into Superset

You must replicate data from your SaaS applications to a data warehouse before you can report on it using Superset. Superset can connect to almost 30 databases and data warehouses. Once you choose a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then specify the database schema or tables you want to work with.

Keeping Desk 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 Desk.com.

And remember, as with any code, once you write it, you have to maintain it. If Salesforce modifies Desk.com's 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.

From Desk.com to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Desk.com data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Desk.com to Redshift, Desk.com to BigQuery, Desk.com to Azure Synapse Analytics, Desk.com to PostgreSQL, Desk.com to Panoply, and Desk.com to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Desk.com with Superset. With just a few clicks, Stitch starts extracting your Desk.com data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.