Mailchimp to Azure SQL Data Warehouse

This page provides you with instructions on how to extract data from Mailchimp and load it into Azure SQL Data Warehouse. (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 MailChimp?

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

What is Azure SQL Data Warehouse?

Azure SQL Data Warehouse is a cloud-based petabyte-scale columnar database service with controls to manage compute and storage resources independently. It offers encryption of data at rest and dynamic data masking to mask sensitive data on the fly, and it integrates with Azure Active Directory. It can replicate to read-only databases in different geographic regions for load balancing and fault tolerance.

Getting data out of MailChimp

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.

Sample MailChimp 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"
    }
  ]
}

Preparing MailChimp 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. 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.

Loading data into Azure SQL Data Warehouse

SQL Data Warehouse provides a multi-step process for loading data. After extracting the data from its source, you can move it to Azure Blob storage or Azure Data Lake Store. You can then use one of three utilities to load the data:

  • AZCopy uses the public internet.
  • Azure ExpressRoute routes the data through a dedicated private connection to Azure, bypassing the public internet by using a VPN or point-to-point Ethernet network.
  • The Azure Data Factory (ADF) cloud service has a gateway that you can install on your local server, then use to create a pipeline to move data to Azure Storage.

From Azure Storage you can load the data into SQL Data Warehouse staging tables by using Microsoft's PolyBase technology. You can run any transformations you need while the data is in staging, then insert it into production tables. Microsoft offers documentation for the whole process.

Keeping MailChimp 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.

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.

Other data warehouse options

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