> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdv/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/sdv/integration/db/integration/bigquery.md).

# ❖ BigQuery

This page describes how to integrate with your BigQuery database.

{% hint style="info" %}
❖ **SDV Enterprise Bundle**. This feature is available as part of the **AI Connectors Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [AI Connectors Bundle](/sdv/explore/sdv-bundles/ai-connectors.md) page.
{% endhint %}

## Installation

To use this feature, please make sure you have installed the bundle with the optional `db-bigquery` dependency. For more information, see the [SDV Enterprise Installation docs](https://docs.sdv.dev/sdv-enterprise/installation/instructions).

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-bigquery
```

## Connector

 Create a connector object that interacts with your BigQuery database.

```python
from sdv.io.database import BigQueryConnector

connector = BigQueryConnector()
```

**Parameters** (None)

**Output** A BigQueryConnector object that you can use to import data and metadata

## Config File

Create a YAML file that corresponds to the database to connect to. You will need separate config files for your source dataset (for importing training data) and a destination dataset (for exporting synthetic data).

The config file has the following structure:

```yaml
connection:
  database_name: <string> 
  schema_name: <string>

auth:
  json_credentials_path: <filepath>
```

**Parameters**:

**`connection`**: This section defines the database tables you want to connect to. It is common to all Google-native databases.

* (required) `database_name`: A top-level logical container for data and database objects within a database system.
* (required) `schema_name`: A named logical grouping of database objects within a database, used for organization and access control.

**`auth`:** This section provides information about your identity, which is required to connect to the database. It is common to all Google-native databases.

* (required) `json_credentials_filepath`: A string with the file that contains the credentials. This can be downloaded for user or service accounts via Google's interface. *Follow the instructions in* [*these docs*](https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account) *to select your account, add a key, and download the JSON file. (If you need help with this step, please contact your database admin.)*

{% hint style="info" %}
Note that the terminology of BigQuery is slightly different from other databases. In the connection parameters: Set the `database_name` to null, and the `schema_name` to your BigQuery's `dataset_id`.
{% endhint %}

## Setting Your Config File

Use the `set_import_config` and `set_export_config` functions to add your config YAML file to your connector.

```python
connector.set_import_config(filepath='source_db_config.yaml')
connector.set_export_config(filepath='destination_db_config.yaml')
```

Use the connector to create metadata, import a subset of training data, and export synthetic data. For more information, see the [Usage API](/sdv/integration/db/api.md).

## FAQ

<details>

<summary>Which permissions are needed for importing and exporting?</summary>

**Importing data requires read access.** For BigQuery, this includes: `bigquery.jobs.create`, `bigquery.tables.get`,  `bigquery.tables.getData`, and `bigquery.tables.list`, `bigquery.datasets.get`. If you do not have these permissions, please contact your database admin.

**Exporting data requires write access.** For BigQuery, this includes: `bigquery.datasets.create`, `bigquery.datasets.get`, `bigquery.jobs.create`, `bigquery.tables.create`, and `bigquery.tables.export`. If you do not have these permissions, please contact your database admin.

</details>

<details>

<summary>Is there a way to speed up import and export?</summary>

Installing `google-cloud-bigquery-storage` in your environment enables the BigQuery Storage API for faster data import/export. This dependency is not currently required so be sure to install it yourself using your preferred package manager.

```
pip install google-cloud-bigquery-storage
```

To see results, make sure you have the `bigquery.readsessions.create` permission.

</details>


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