> ## Documentation Index
> Fetch the complete documentation index at: https://docs.featureform.com/llms.txt
> Use this file to discover all available pages before exploring further.

# BigQuery

> Featureform supports [BigQuery](https://cloud.google.com/bigquery) as an Offline Store.

## Implementation

### Primary Sources

#### Tables

Table sources are used directly via a view. Featureform will never write to a primary source.

### Transformation Sources

SQL transformations are used to create a view. By default, those views are materialized and updated according to the schedule parameter. Deprecated transformations are converted to un-materialized views to save storage space.

### Offline to Inference Store Materialization

When a feature is registered, Featureform creates an internal transformation to get the newest value of every feature and its associated entity. A Kubernetes job is then kicked off to sync this up with the Inference store.

### Training Set Generation

Every registered feature and label is associated with a view table. That view contains three columns, the entity, value, and timestamp. When a training set is registered, it is created as a materialized view via a JOIN on the corresponding label and feature views.

## Configuration

First we have to add a declarative BigQuery configuration in Python.

```python bigquery_config.py theme={null}
import featureform as ff

bigquery = ff.register_bigquery(
    name="bigquery-quickstart",
    description="A BigQuery deployment we created for the Featureform quickstart",
    project_id="",
    dataset_id="",
    credentials_path=""
)
```

Once our config file is complete, we can apply it to our Featureform deployment

```python theme={null}
featureform apply bigquery_config.py --host $FEATUREFORM_HOST
```

We can re-verify that the provider is created by checking the [Providers tab of the Feature Registry](/getting-started/exploring-the-feature-registry).

### Mutable Configuration Fields

* `description`

* `credentials`