Desk to Snowflake

This page provides you with instructions on how to extract data from Desk 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 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 Snowflake?

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.

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.

Preparing data for Snowflake

Depending on the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.

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

Loading data into Snowflake

The Snowflake documentation's Data Loading Overview section can help you with the task of loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are the limitations on that tool will make it a non-starter as a reliable ETL solution. Alternatively, there are two main steps for getting data into Snowflake:

  • 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. One of Snowflake's slick features lets you make a virtual warehouse that can power the insertion process.

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.

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, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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, and To Panoply.

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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Desk data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.