Iterable to Redshift

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

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": "",
   "eventName": "emailUnSubscribe",
   "dataFields": {
      "unsubSource": "EmailLink",
      "email": "",
      "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": ""

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.

Loading data into Redshift

Once you know all of the columns you want to insert, use the CREATE TABLE statement in the Redshift data warehouse to set up a table to receive all the data.

Next, migrate your data. It may seem like the easiest course would be to build INSERT statements to add data to your Redshift table row by row. That would be a mistake; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to copy the data into Amazon S3 and then use the COPY command to load it into Redshift.

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

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, or Snowflake, 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 BigQuery, To Postgres, To Snowflake, 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 Redshift data warehouse.