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Features

This guide introduces you to the concept of features in Profiles and explains how to define them in your Profiles project.

Overview

A Customer360 (C360) table contains various customer features. In Profiles, you can create these features using entity_vars which, in turn, can be created using an input, model or other entity_vars.

Usage

You can define features in the profiles.yaml file, as shown:

var_groups:
  - name: user_features
    entity_key: user
    vars:
      - entity_var:
          name: first_seen
          select: min(cast(timestamp as date))
          from: inputs/rsPages
          is_feature: false
          description: First seen date
      - entity_var:
          name: last_seen
          select: max(cast(timestamp as date))
          from: inputs/rsPages
          description: Last seen date
      - entity_var:
          name: user_lifespan
          select: '{{ user.last_seen }} - {{ user.first_seen }}'
          description: User lifespan

Incremental features

This is a beta feature.

Profiles also supports Incremental Features to help reduce warehouse costs, especially in case of large datasets.

By adding a merge: clause to your entity_var definitions and marking the input model as is_append_only: true, you enable Profiles to process only new data and merge it with previously computed values — improving performance and reducing costs considerably.

Controlling update frequency

Not all features need to be computed on every run — use Timegrains to control how often features are updated:

var_groups:
  - name: realtime_signals
    entity_key: user
    time_grain: day       # Computed daily
    vars:
      - entity_var:
          name: last_active_date
          select: max(timestamp::date)
          from: inputs/rsPages

  - name: monthly_aggregates
    entity_key: user
    time_grain: month     # Computed monthly
    vars:
      - entity_var:
          name: favorite_category
          select: mode(category)
          from: inputs/orders

This helps reduce compute costs for features that don’t need frequent updates.

Best practices

• An entity_var will automatically group records from a source input or model by customer, so you need only to specify an aggregate function in the select field.
• Every entity_var is included in feature_views by default.
• To use an entity_var to create another entity_var but not include it as a feature, add is_feature: false to the entity_var definition.
• For large datasets, consider making features incremental to improve performance.
• Group features by update frequency using time_grain on var_group to optimize compute costs.