Iterable to Snowflake

This page provides you with instructions on how to extract data from Iterable and load it into Snowflake. (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 Iterable?

Iterable hosts a growth marketing platform that provides omnichannel customer engagement through email, SMS, web push, and other channels. Marketers can use a drag-and-drop interface to set up campaign workflows.

What is Snowflake?

Snowflake is a cloud-native data warehouse that runs on an Amazon Web Services platform. Snowflake is designed to be fast, flexible, and easy to work with. It provides native support for JSON, Avro, XML, and Parquet. Users pay for only the storage and compute resources they use, and can scale storage and compute resources separately.

Getting data out of Iterable

Iterable exposes data through webhooks, which you can create at Integrations > Webhooks. You must specify the URL the webhook should use to POST data, and choose an authorization type. Edit the webhook, tick the Enabled box, select the events you'd like to send data to the webhook for, and save your changes.

Sample Iterable data

Iterable returns data in JSON format. Here’s an example of the data returned for an email unsubscribe event:
{
   "email": "sheldon@iterable.com",
   "eventName": "emailUnSubscribe",
   "dataFields": {
      "unsubSource": "EmailLink",
      "email": "sheldon@iterable.com",
      "createdAt": "2017-12-02 22:13:05 +00:00",
      "campaignId": 59667,
      "templateId": 93849,
      "messageId": "d3c44d47b4994306b4db8d16a94db025",
      "emailSubject": "Welcome to JM Photography at {{now}}",
      "campaignName": "Test the NOW handlebars",
      "workflowId": null,
      "workflowName": null,
      "templateName": "Sample photography welcome",
      "channelId": 3420,
      "messageTypeId": 3866,
      "experimentId": null,
      "emailId": "c59667:t93849:sheldon@iterable.com"
   }
}

Preparing Iterable 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. Iterable'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. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Preparing data for Snowflake

Depending on how your data is structured, you may need to prepare it for loading. Read about the supported data types for Snowflake and make sure that your data maps well to them.

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

Loading data into Snowflake

Snowflake's documentation includes a Data Loading Overview that guides you through the task of loading your data. A data loading wizard in the Snowflake web UI may be useful if you're not loading a lot of data, but for many organizations, the limitations on that tool will make it unsuitable. You can load your data with two manual steps:

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

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

Keeping Iterable data up to date

Once you've set up the webhooks you want and have begun collecting data, you can relax – as long as everything continues to work correctly. You'll have to keep an eye out for any changes to Iterable's webhooks implementation.

Other data warehouse options

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, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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, and To Panoply.

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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Iterable data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.