ID Mappings Data
Cortex extends the value of your business’s unique data by enabling anyone to transform that data into Machine Learning Pipelines. The types of data supported by Cortex fall into five categories: events, attributes, subscriptions, items, and ID mappings.
|Events *||Timestamped actions taken by your users.||Customer ABC completes a purchase event at time T.|
|Attributes||Characteristics or traits of your users.||Customer ABC has job title ‘Professor’, and age 49.|
|Subscriptions||Records of which users subscribed to which offerings and when.||Customer ABC has subscription ‘Premium’ with start time T and end time null.|
|Items||Metadata attributes for the items that your users interact with, via events||Item XYZ has category ‘shoes’, and price 49.99.|
|ID Mappings||Associations between one set of IDs and another.||Customer ABC has cookie ID 123.|
In this guide, we’ll provide an overview of ID Mappings data and discuss the various ways that it may be ingested into Cortex.
What are ID mappings?
ID Mappings provide instructions so that Cortex can map one set of user IDs to another. If you collect events or attributes data from disparate sources that operate in different ID spaces, ID mappings can help unify this data into a single user profile atop which predictions can be made.
The below table shows an example of what ID Mappings data might look like. Each row contains two pieces of information: (1) an original ID that should be mapped to a new ID space, and (2) a mapped ID which should be substituted for the original ID. Note that many original_ids can be mapped to the same mapped_id, but an original_id should only be associated with one mapped_id.
How do I send ID Mappings data into Cortex?
1) Batch File Uploads
ID Mappings data can be batch uploaded to Cortex in files exported from, say, your Customer Data Platform or data lake. To do this, schedule a recurring file upload into a cloud-based directory hosted by either you or Vidora. Cortex provides flexibility around both the format of these files (CSV, JSON, Parquet, etc.) as well as the type of directory into which they’re uploaded (Amazon S3, Google Cloud, Azure, etc.).
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