MailChimp is a marketing automation platform and email marketing service that companies use to send more than a billion email messages a day.

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.

MailChimp also offers a RESTful API for syncing campaign information and stats. To get your MailChimp data into your data warehouse, you can extract it from MailChimp's servers using the MailChimp API. To get information about a campaign with the API, for example, you could call GET /campaigns/{campaign_id}.

MailChimp generates a lot of data. Anytime you send an email or someone opens and reads one, it generates an event. Depending on your needs, you may want to use webhooks to receive streaming updates of these events as they happen. If so, you'll need to build code on your end to receive the streaming data.

The MailChimp API returns JSON-formatted data. A call to get campaign information might return data that looks like this.

{
  "id": "42694e9e57",
  "type": "regular",
  "create_time": "2018-12-15T14:40:36+00:00",
  "archive_url": "http://eepurl.com/xxxx",
  "status": "save",
  "emails_sent": 0,
  "send_time": "",
  "content_type": "template",
  "recipients": {
    "list_id": "57afe96172",
    "segment_text": ""
  },
  "settings": {
    "subject_line": "I have a watermelon farm.",
    "title": "Freddie's Jokes Vol. 1",
    "from_name": "Freddie",
    "reply_to": "freddie@freddiesjokes.com",
    "use_conversation": false,
    "to_name": "",
    "folder_id": 0,
    "authenticate": true,
    "auto_footer": false,
    "inline_css": false,
    "auto_tweet": false,
    "fb_comments": false,
    "timewarp": false,
    "template_id": 100,
    "drag_and_drop": true
  },
  "tracking": {
    "opens": true,
    "html_clicks": true,
    "text_clicks": false,
    "goal_tracking": true,
    "ecomm360": true,
    "google_analytics": true,
    "clicktale": ""
  },
  "delivery_status": {
    "enabled": false
  },
  "_links": [
    {
      "rel": "parent",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Collection.json",
      "schema": "https://api.mailchimp.com/schema/3.0/CollectionLinks/Campaigns.json"
    },
    {
      "rel": "self",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Instance.json"
    },
    {
      "rel": "delete",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57",
      "method": "DELETE"
    },
    {
      "rel": "cancel_send",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57/actions/cancel-send",
      "method": "POST"
    },
    {
      "rel": "feedback",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57/feedback",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Feedback/Collection.json"
    }
  ]
}

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. MailChimp'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. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

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.

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.

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

And remember, as with any code, once you write it, you have to maintain it. If MailChimp modifies its 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.

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 Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. 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, To S3, and To Delta Lake.