Bold BI dashboard designer supports connecting Google BigQuery server using the Live mode.
OAuth
Service Account
Create a service account:
https://console.cloud.google.com/projectselector2/iam-admin/serviceaccounts
In the Service account description field, enter a description. For example, Service account for quickstart.
Click Create and continue.
To provide access to your project, grant the following role(s) to your service account: Project > Owner.
For additional roles, click add Add another role and add each additional role.
NOTE: The Role field affects which resources your service account can access in your project. You can revoke these roles or grant additional roles later. In production environments, do not grant the Owner, Editor, or Viewer roles in production environments. Instead, grant a predefined role or custom role that meets your needs.
Create a service account key:
In the Google Cloud console, click the email address for the service account that you created.
Click context menu and select Manage Keys.
1. Go to your personal or organization accounts for Google developer console and create a project or application
Google BigQuery console link: https://console.cloud.google.com/bigquery
You can see the projects list as follows.
Create a new project as follows.
2. Enable a Google BigQuery API
Go to the following link and enable the Google developers console API.
https://console.developers.google.com/apis/library/people.googleapis.com
You can edit or view your OAuth grant limit, redirect URI, scope, authorized domains, and more using the following link.
https://console.cloud.google.com/apis/credentials/consent
Select OAuth Consent screen tab and choose external user type as shown in the following image.
Enter the app’s name and user’s email address, as shown in the following image.
Enter the app domain information for registering the application with Bold BI as follows.
Set the authorized domain as boldbi.com and enter the User’s email address mail for development contact information as shown in the following image.
Add Test Users
Click Add Users and enter the user’s email address and click Save, as shown in the following image.
To Create a new Credentials (Client ID, Client Secret), use the following link.
https://console.cloud.google.com/apis/credentials
After creating the credentials, open the corresponding credentials and add your Bold BI Enterprises build hosted URL as the following sample URL format.
http://localhost:<boldbi-running port>/bi/designer/v1.0/oauth/agent
3. Go to Bold BI application and navigate to data connectors settings page
If intermediate DB is not configured, you will get an error as follows.
Then, configure the intermediate DB in Data Store section.
Also, configure the client ID and client secret information in Connectors section and save the information for Google BigQuery as follows.
Once the pre-requisites done, you are good to go to work with Google BigQuery.
To configure the Google BigQuery data source, follow these steps:
Click the Data Sources button in the configuration panel to add a new data connection.
Click CREATE NEW to launch a new connection from the connection panel.
Select the Google BigQuery connection in the connection panel.
NOTE: You can also create a data source from the home page by clicking the Data Sources menu from left menu panel and Create Data Source from the data sources page.
Use the following steps to authenticate with Google BigQuery server:
Click the data source, you will be prompted with a login window. Enter the credentials of your Google BigQuery account to authorize.
Click Allow in the authorization window to accept the scopes requested by Bold BI dashboards.
NOTE: If the permission is denied, the account will not be accessible from the dashboard.
Set a name to identify the account and click Next.
Now, you have successfully authorized your connection. Click Connect to continue with the data source connection.
To connect using the already connected account, refer to Connected Accounts.
NOTE: To connect to another account, click Connect New Account.
After successful authentication, the NEW DATA SOURCE configuration panel opens.
Follow these steps to create the Google BigQuery data source.
Enter a name and description (optional) for the data source.
Choose the required projects in Project drop down box.
The authentication type will be set to Google BigQuery automatically in Connected as text box since OAuth is used for authenticating with Google BigQuery account.
To edit the connection information, use the Edit Connection option
For Service Authentication:
Select the JSON file downloaded from Connecting with Service Authentication
For OAuth:
The authentication type will automatically be sent to the Google BigQuery in the Connected as text box since the OAuth is used for authenticating with the Google BigQuery account.
Choose the required projects in the Project drop-down box.
To connect the Google Big Query with a particular dataset, enter the property currentdataset={datasetname} or specificdataset={datasetname} in the Additional connection parameters text box.
To edit the connection information, use the Edit Connection option.
The available schemas list is shown in tree view for the selected projects that are retrieved from the Google BigQuery server.
In the data design view page, drag and drop the table.
In the data design view page, drag, and drop the table.
You can use Code View options for passing query to display data.
Click Save to save the data source with a relevant name.
If you have already logged into the account and authenticated with the data source, the account information will be listed here. You can select one of the accounts or connect to a new account by clicking the Connect New Account button.
You can edit, delete, and re-authorize this account from the Connected Accounts page.
Google BigQuery is a data warehouse and only supports live connection in Bold BI. You can link your Google BigQuery with Google Analytics by following the official documentation steps about linking Google BigQuery with Google Analytics.
Dashboard Designer Walkthrough
Google BigQuery Limitations to build query