# Feature view

## Feature View

A feature view is an object that represents a logical group of time-series feature data as it is found in a [data source](/v0.12-branch/getting-started/concepts/data-source.md). Feature views consist of one or more [entities](/v0.12-branch/getting-started/concepts/entity.md), [features](/v0.12-branch/getting-started/concepts/feature-view.md#feature), and a [data source](/v0.12-branch/getting-started/concepts/data-source.md). Feature views allow Feast to model your existing feature data in a consistent way in both an offline (training) and online (serving) environment.

{% tabs %}
{% tab title="driver\_trips\_feature\_view\.py" %}

```python
driver_stats_fv = FeatureView(
    name="driver_activity",
    entities=["driver"],
    features=[
        Feature(name="trips_today", dtype=ValueType.INT64),
        Feature(name="rating", dtype=ValueType.FLOAT),
    ],
    batch_source=BigQuerySource(
        table_ref="feast-oss.demo_data.driver_activity"
    )
)
```

{% endtab %}
{% endtabs %}

Feature views are used during

* The generation of training datasets by querying the data source of feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.
* Loading of feature values into an online store. Feature views determine the storage schema in the online store.
* Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.

{% hint style="info" %}
Feast does not generate feature values. It acts as the ingestion and serving system. The data sources described within feature views should reference feature values in their already computed form.
{% endhint %}

## Feature

A feature is an individual measurable property observed on an entity. For example, a feature of a `customer` entity could be the number of transactions they have made on an average month.

Features are defined as part of feature views. Since Feast does not transform data, a feature is essentially a schema that only contains a name and a type:

```python
trips_today = Feature(
    name="trips_today",
    dtype=ValueType.FLOAT
)
```

Together with [data sources](/v0.12-branch/getting-started/concepts/data-source.md), they indicate to Feast where to find your feature values, e.g., in a specific parquet file or BigQuery table. Feature definitions are also used when reading features from the feature store, using [feature references](/v0.12-branch/getting-started/concepts/feature-retrieval.md#feature-references).

Feature names must be unique within a [feature view](/v0.12-branch/getting-started/concepts/feature-view.md#feature-view).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.feast.dev/v0.12-branch/getting-started/concepts/feature-view.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.