Customer.io powers email, SMS, and other customer interactions with a rules-based engine that automates communication distribution.

Snowflake is a cloud-based data warehouse implemented as a managed service. It runs on the Amazon Web Services architecture, and separates compute and storage resources, enabling users to scale the two independently and pay only for resources used. It can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

Customer.io publishes information about email activity through webhooks, which you can set up through its management interface. You can select from more than a dozen events to trigger a data exchange.

Customer.io sends the information it returns in JSON format in an HTTP POST. Each JSON object may contain dozens of attributes, which you have to parse before loading the data into your data warehouse. Here's an example of what data might look like for email-related events:

{
"data": {
  "campaign_id": "1000002",
  "campaign_name": "Upgrade to Premium",
  "customer_id": "98513",
  "email_address": "customer@example.com",
  "email_id": "NTE4MzE6FwGLxwJkAAJkABcBIfcaAVVvdGukFUsYV2hY6QFlOjQ4YTZhODljLTM3MjktMTFlNi04MDQwLTYzNGY3NzAzM2NhNjozNDMwMzEA",
  "message_id": "1000013",
  "message_name": "First Upgrade Email",
  "subject": "Have any doubts?",
  "template_id": "343031",
  "variables": {
    "attachments": null,
    "customer": {
      "created_at": 1466453747,
      "email": "customer@example.com",
      "id": 98513,
      "name": "John Doe",
      "plan_name": "free"
    },
    "email_id": "NTE4MzE6FwGLxwJkAAJkABcBIfcaAVVvdGukFUsYV2hY6QFlOjQ4YTZhODljLTM3MjktMTFlNi04MDQwLTYzNGY3NzAzM2NhNjozNDMwMzEA",
    "event": {
      "page": "https://customer.io/pricing/"
    },
    "event_id": "48a6a89c-3729-11e6-8040-634f77033ca6",
    "event_name": "viewed_pricing_page",
    "from_address": null,
    "recipient": null,
    "reply_to": null
  }
},
"event_id": "b50cb221c60f87cdf06e",
"event_type": "email_drafted",
"timestamp": 1466456299
}

You may need to prepare your data before loading it. Check Snowflake's supported data types and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Snowflake's documentation outlines a Data Loading Overview that can lead you through the task of loading your data. If you're not loading a lot of data, Snowflake's data loading wizard may be helpful, but for many organizations, its limitations make it unacceptable. Instead, you can:

• Use the PUT command to stage files.
• Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Customer.io.

And remember, as with any code, once you write it, you have to maintain it. If Customer.io 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.

Snowflake 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 Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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 Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.