Iterable to QuickSight

This page provides you with instructions on how to extract data from Iterable and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Iterable seem too complex or difficult to maintain, 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 QuickSight?

Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.

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 QuickSight

You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.

Using data in QuickSight

QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.

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.

From Iterable to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Iterable data in Amazon QuickSight is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Iterable to Redshift, Iterable to BigQuery, Iterable to Azure SQL Data Warehouse, Iterable to PostgreSQL, Iterable to Panoply, and Iterable to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Iterable with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Iterable data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Amazon QuickSight.