# BigQuery

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## Setup Google Cloud Platform Service Account and Grant Access (for New Users)

To add a Google BigQuery Data Warehouse as a Data Source on AirOps, use a Google Cloud Platform service account with **read** access to the desired datasets and tables:

### 1. Create a Google Cloud Platform Service Account

1. Go to the [Google Cloud Platform Console](https://console.cloud.google.com/).
2. Click on the project dropdown and select the project that contains your BigQuery dataset.
3. Navigate to the **IAM & Admin** page, and then click **Service accounts**.
4. Click **+ CREATE SERVICE ACCOUNT** at the top of the page.
5. Enter a name for your service account and click **Create**.
6. On the **Grant this service account access to the project** page, select the **BigQuery Data Viewer** and **BigQuery Job User** roles.
7. Click **Continue**, and then click **Done**.

### 2. Generate a JSON key for the Service Account

1. In the **Service accounts** page, find the service account you just created.
2. Click the **Actions** menu (three vertical dots) and select **Create key**.
3. Select **JSON** as the key type, and click **Create**.
4. Save the generated JSON key file to your computer.

<figure><img src="https://files.readme.io/b48622d-image.png" alt="" width="375"><figcaption></figcaption></figure>

### 3. Enter the JSON Key File Content in AirOps

1. Open the JSON key file with a text editor.
2. Copy the entire content of the JSON key file.
3. In AirOps, paste the JSON key file content into the **JSON Key** field.

Now, your Google BigQuery Data Warehouse should be connected and ready for use within AirOps.


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# 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.airops.com/context/memory-stores/add-data/import-from-sql-database/data-sources/bigquery.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.
