Campaign Monitor is a SaaS email marketing platform that enables businesses to create, send, and manage branded email. The product includes a drag-and-drop builder, visual journey designer, and real-time performance metrics. It offers list management tools to help organizations build segmented lists based on their own criteria, and optimization tools to help users track the effectiveness of their campaigns.

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 Campaign Monitor's RESTful API to get data about clients, campaigns, lists, and more into your data warehouse. For example, to get a campaign summary, you could GET GET /campaigns/{campaignid}/summary.{xml|json}. The final parameter specifies whether the data is to be returned in XML or JSON format.

Here's an example of the kind of response you might see when querying a campaign summary and specifying that the data returned should be in JSON format.

{
    "Recipients": 1000,
    "TotalOpened": 345,
    "Clicks": 132,
    "Unsubscribed": 43,
    "Bounced": 15,
    "UniqueOpened": 298,
    "SpamComplaints": 23,
    "WebVersionURL": "http://createsend.com/t/y-A1A1A1A1A1A1A1A1A1A1A1A1/",
    "WebVersionTextURL": "http://createsend.com/t/y-A1A1A1A1A1A1A1A1A1A1A1A1/t",
    "WorldviewURL": "http://myclient.createsend.com/reports/wv/y/8WY898U9U98U9U9",
    "Forwards": 18,
    "Likes": 25,
    "Mentions": 11
}

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 Campaign Monitor and loads it into your data warehouse, but what happens tomorrow when you have new campaigns?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Campaign Monitor's API results include fields like Date 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.