Front to Panoply

This page provides you with instructions on how to extract data from Front and load it into Panoply. (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 Front?

Front lets you manage all of your communication channels – email, social media, chat, SMS – in one place, and helps your team collaborate around messages. You can comment on email threads within shared inboxes like support@yourcompany.com without those comments being visible to the sender, and without having to forward or reply-all. You can assign emails to individuals, and set reminders to respond later. Front also offers email templates, sequences, mail merge, and shortcuts to automate your workflow.

What is Panoply?

Panoply is a fully managed data warehouse service that can spin up an Amazon Redshift instance in just a few clicks. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. With Panoply, you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Front

You can use Front's API to get data about teams, conversations, and many more tables. For example, to get information about a team, you could GET https://api2.frontapp.com/teams/{team_id}.

Sample Front data

Here's an example of the kind of response you might see when querying a team.

{
  "_links": {
    "self": "https://api2.frontapp.com/teams/tim_55c8c149"
  },
  "id": "tim_55c8c149",
  "name": "Delivery",
  "inboxes": [
    {
      "_links": {
        "self": "https://api2.frontapp.com/inboxes/inb_55c8c149",
        "related": {
          "teammates": "https://api2.frontapp.com/inboxes/inb_55c8c149/teammates",
          "conversations": "https://api2.frontapp.com/inboxes/inb_55c8c149/conversations",
          "channels": "https://api2.frontapp.com/inboxes/inb_55c8c149/channels",
          "owner": "https://api2.frontapp.com/teams/tim_55c8c149"
        }
      },
      "id": "inb_55c8c149",
      "name": "Team",
      "is_private": false
    }
  ],
  "members": [
    {
      "_links": {
        "self": "https://api2.frontapp.com/teammates/tea_55c8c149",
        "related": {
          "inboxes": "https://api2.frontapp.com/teammates/tea_55c8c149/inboxes",
          "conversations": "https://api2.frontapp.com/teammates/tea_55c8c149/conversations"
        }
      },
      "id": "tea_55c8c149",
      "email": "leela@planet-express.com",
      "username": "leela",
      "first_name": "Leela",
      "last_name": "Turanga",
      "is_admin": true,
      "is_available": true,
      "is_blocked": false
    }
  ]
}

Loading data into Panoply

Once you have identified all of the columns you want to insert, you can use the CREATE TABLE statement in Panoply's Redshift data warehouse to create a table to receive all of the data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Keeping Front data up to date

Now what? You've built a script that pulls data from Front and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, many of Front's API results include fields like created_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

Panoply 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, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. 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 Snowflake, To Azure SQL Data Warehouse, and To S3.

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 Front to Panoply automatically. With just a few clicks, Stitch starts extracting your Front data via the API, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.