# BigQuery setup
Recurve's BigQuery connector enables you to seamlessly integrate your BigQuery data warehouse as a destination for your analytics database. This guide walks you through establishing a secure connection using Google Cloud service accounts, from initial setup to your first successful connection.
You'll learn how to:
* Prepare a sample dataset in your Google Cloud Project (GCP).
* Create and configure a service account with sufficient permissions.
* Establish a connection between Recurve and BigQuery.
### Prerequisites
* You already join a Recurve organization.
* You've already created a [GCP project](https://developers.google.com/workspace/guides/create-project).
### Prepare sample dataset
To demonstrate the connetion process, we'll be using the `jaffle_shop` dataset — a fictional e-commerce store's data provided by the dbt community. This dataset offers a practical example of typical e-commerce data structures that mirror real-world scenarios.
You can use the [jafgen](https://github.com/dbt-labs/jaffle-shop-generator) CLI tool generate synthetic data for any specified year range.
The dataset includes these tables:
* Customers (who place Orders)
* Orders (from those Customers)
* Products (the food and beverages the Orders contain)
* Order Items (of those Products)
* Supplies (needed for making those Products)
* Stores (where the Orders are placed and fulfilled)
* Tweets (Customers sometimes issue Tweets after placing an Order)
Or simply download the generated data below:
{% file src="/files/oYDfqEhOrOPCXZBLY5sJ" %}
### Ingest sample data
Follow these steps to ingest the data:
1. Go to your [GCP project console](https://console.cloud.google.com/bigquery).
2. Create a new dataset:
1. Expand the *Actions* option and select [**Create dataset**](https://cloud.google.com/bigquery/docs/datasets#create-dataset).
2. Enter provide a name for the dataset in **Dataset ID**. You can leave the other fields as default.
3. Click **Create dataset**.
3. Create tables for the dataset.
1. Click on the *Actions* option of the dataset and select **Create table**.
2. Choose **Upload** as the creation method, select the CSV file, and name the table after the file name. For example, we upload the `raw_orders.csv` and name the table `raw_orders`.
3. In **Schema**, check **Auto detect** to automatically generate the schema for the table.
4. Click **Create table**.
5. Repeat the four steps above to create and upload the other tables.
### Create a service account
After loading data into the project, you need to create a Google Service Account. This account represents the identity and permissions that the connector can use to authenticate and interact with BigQuery.
Follow these steps:
1. Go to the [GCP credentials wizard](https://console.cloud.google.com/apis/credentials/wizard) and select your project.
2. Enable BigQuery API:
1. Select **BigQuery API** from the dropdown.
2. Choose **Application data** for data processing type.
3. Click **Next**. This navigates to to the **Create service account** section.
3. Configure your service account:
1. Provide a descriptive name for your service account.
2. Click **Create and continue**.
3. Assign the two following roles:
* BigQuery Job User.
* BigQuery Data Editor.
4. Click **Done**.
4. After the service account is created, the console navigates you to the [Credentials](https://console.cloud.google.com/apis/credentials) page, where you can generate keys to authenticate the account. Locate the **Service Accounts** rows and click on the account
1. Navigate to the **Keys** tab.
2. Click the **Add key** -> **Create new key**.
3. Select **JSON** as the **Key type** and click **Create**. This will download the JSON file that contains authentication credentials and metadata.
### Connect Recurve to BigQuery
With the Google Service Account key, you now can set up a connection to BigQuery from Recurve.
Follow these steps:
1. Open the left sidebar.
2. Navigate to **Connections** -> **Destinations**.
3. Click **+ Create connection**.
4. Select **Google BigQuery**.
5. Fill in the fields with the information from the JSON key file.
* **Destination name**: the name to identify this specific connection in your organization.
* **Auth Type**: `service_account`
* **Google Project ID**: the unique identifier for your GCP project
* **Google Auth Private Key ID**: the unique identifier for your service account's private key
* **Google Auth Private Key**: the actual private key used for authentication
* **Client ID**: your service account's unique identifier
* **Client Email**: the email address associated with your service account
6. Select **Development environment** and click **Test connection**.
7. Once the connection is successfully tested, click **Create Destination**.
The connection is now ready to be used in a project.
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