Iterable to Panoply

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

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.

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.

Loading data into Panoply

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.

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

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, or Snowflake, which are RDBMSes that use similar SQL syntax. 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 Snowflake.

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 Panoply data warehouse.