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.

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

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}.

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
    }
  ]
}

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

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.

Delta Lake on Databricks 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 Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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 Panoply, and To S3.