Iterable to Databricks

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

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

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 Delta Lake on Databricks

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole 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.

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 move data from Iterable to Delta Lake on Databricks automatically. 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 your Delta Lake on Databricks data warehouse.