# 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`]({{< ref "profiles/dev-docs/profiles-yaml/var-groups/entity-var.md" >}}) which, in turn, can be created using an [`input`]({{< ref "profiles/concepts/inputs.md" >}}), `model` or other `entity_vars`.

## Usage

You can define features in the [`profiles.yaml`]({{< ref "profiles/dev-docs/profiles-yaml/" >}}) file, as shown:

```yaml
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

{{< announcement >}}
This is a beta feature.
{{< /announcement >}}

Profiles also supports [Incremental Features]({{< ref "profiles/concepts/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]({{< ref "profiles/concepts/timegrains.md" >}}) to control how often features are updated:

```yaml
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`]({{< ref "profiles/concepts/feature-views.md" >}}) 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.

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