> ## Documentation Index
> Fetch the complete documentation index at: https://docs2.zenskar.com/llms.txt
> Use this file to discover all available pages before exploring further.
# BigQuery
## Overview
[Google BigQuery](https://cloud.google.com/bigquery) is a fully managed, serverless data warehouse that enables scalable analysis over large datasets. In Zenskar, you can use BigQuery as a **data source** for billable metrics and dashboards.
***
## Prerequisites
* [Enable BigQuery API in your GCP](https://cloud.google.com/apis/docs/enable-disable-apis) before connecting it to Zenskar. [You can enable BigQuery API directly from your project too.](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).
* [Create a service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts).
* [Add the Service Account as a **Member** in your GCP account with the **BigQuery User** role](https://cloud.google.com/iam/docs/granting-changing-revoking-access#granting-console).
* Similarly, assign the **BigQuery Data Editor** role to the Service Account.
We highly recommend that you create a Service Account exclusive to Zenskar for ease of permission and auditing. However, you can use a pre-existing Service Account that has the correct permissions.
### Create and manage a Service Account key in Google Cloud Project
* [Service Account keys](https://cloud.google.com/iam/docs/service-accounts#service_account_keys) are used to authenticate as Google Service Accounts. Zenskar requires the Service Account keys to leverage the role-based permissions you granted to the Service Account in the previous section.
* [Create and manage a Service Account key](https://cloud.google.com/iam/docs/creating-managing-service-account-keys). Ensure the following:
* **Create your key in JSON format**.
* Download the key immediately.
* Google will not allow you to see the contents of the key once you navigate away.
Delete the downloaded key file after setup for security.
***
## Set up a BigQuery data-source connector via Zenskar dashboard
1. Log into your Zenskar dashboard.
2. In the left side bar, click **Usage** > **Data Sources**.
3. In the top-right corner, click **+ ADD DATA SOURCE**.
4. On the **Add New Data Source** page, [configure the BigQuery connector](https://docs.zenskar.com/docs/data-source-connector-for-bigquery#connector-configuration-1).
5. Click on the **SAVE SOURCE** button.
### Connector configuration
#### General configuration
| Field | Description | Required |
| :-------------- | :------------------------------------------ | :------- |
| **Source Name** | Enter a unique name for this data source. | Yes |
| **Source Type** | Select **BigQuery** from the dropdown menu. | Yes |
#### Connector configuration
| Field | Description | Required |
| :------------------- | :-------------------------------------------------------------------------------------------------------------------------------- | :------- |
| **Project ID** | The ID of your Google Cloud project that contains the BigQuery datasets. | Yes |
| **Dataset ID** | The dataset you want to query. | Yes |
| **Location** | The [geographic location of your BigQuery dataset](https://cloud.google.com/bigquery/docs/locations) (for example, `US` or `EU`). | Yes |
| **Credentials JSON** | Paste the contents of your **Service Account Key** (JSON format). | Yes |
#### Data source access mode (read-only)
Zenskar queries data directly from BigQuery without syncing it to Zenskar’s data infrastructure. This option is:
* Ideal for **large databases** (more than 30 GB)
* Suitable for **real-time data access**
* **No sync waiting time**
You will be able to browse and query tables from this BigQuery source in the **Data Navigator** while creating aggregates.
***
## Set up a BigQuery data-source connector via API
The `connector_config` object is the only part of the request that differs across connector types (such as BigQuery, Snowflake, or Redshift). The [create data-source connector API reference](https://www.zenskar.com/reference/create-data-source-connector) provides a generic overview, while this document explains the BigQuery-specific structure of `connector_config`.
### Request example
```curl theme={null}
curl --location 'https://api.zenskar.com/datasources' \
-H 'x-api-key: ' \
-H 'apiversion: 20240301' \
-H 'Content-Type: application/json' \
-d '{
"name": "abc",
"connector_type": "BigQuery",
"destination": "BigQuery",
"status": "active",
"connector_config": {
"project_id": "my-test-project-84030",
"dataset_id": "abc123",
"location": "US",
"credentials_json": "{ \"type\": \"service_account\", \"project_id\": \"my-test-project-84030\", \"private_key_id\": \"0533306f55057\", \"private_key\": \"-----BEGIN PRIVATE KEY-----\\t/YgHKB4tH\\n-----END PRIVATE KEY-----\\n\", \"client_email\": \"example@my-test-project-84030.iam.gserviceaccount.com\", \"token_uri\": \"https://oauth2.googleapis.com/token\" }"
},
"source_definition_id": "bfd1ddf8-ae8a-4620-b1d7-55597d2ba08c",
"remote_conn": true
}'
```
#### BigQuery-specific connector configuration
The `connector_config` object contains configuration fields specific to the BigQuery data source.
| Field | Description | Required |
| ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- |
| `project_id` | The unique ID of your Google Cloud project that contains the BigQuery datasets. | Yes |
| `dataset_id` | The dataset name (for example, `abc123`). **Do not** include the project ID prefix (such as `my-test-project-84030.abc123`). The project ID is already supplied separately. | Yes |
| `location` | The geographic location of your BigQuery dataset (for example, `US` or `EU`). | Yes |
| `credentials_json` | The complete contents of your Service Account key, formatted as a **stringified JSON** (escaped quotes and newlines). | Yes |
* Some users may specify `dataset_id` in the format `.` (for example, `my-test-project-84030.abc123`), which is valid in BigQuery but **not supported in Zenskar**. Pass only the dataset name (`abc123`), as `project_id` is already handled separately.
* Ensure that the Service Account used has the **BigQuery User** and **BigQuery Data Editor** roles.
* Zenskar supports only **JSON-format** Service Account keys.
***
## Notes
Zenskar does **not modify** your existing BigQuery data or schema.
Queries are executed using your service account’s permissions.
For best performance, use partitioned or clustered tables when working with large datasets.