Skip to main content Connect your Google BigQuery data warehouse to Basedash to analyze and visualize your enterprise data.
Prerequisites Connection setup
From your Basedash dashboard, click “Add Data Source”
Select “BigQuery” as your data warehouse
Upload your service account key file
Select or enter your project ID
Choose default dataset (optional)
Click “Test Connection” to verify
Save your connection
Required permissions Your service account needs the following IAM roles:
roles/bigquery.dataViewer - to read dataroles/bigquery.jobUser - to run queriesroles/bigquery.resourceViewer - to list projects and datasets
Best practices
Create a dedicated service account for Basedash
Grant minimum required permissions
Regularly rotate service account keys
Use table partitioning for large datasets
Set appropriate query cost limits
Use clustered and partitioned tables
Materialize commonly used views
Create appropriate table statistics
Monitor query costs and performance
Set up appropriate caching policies
Troubleshooting Next steps: Add custom context You can add custom context to help the AI better understand your data structure and business logic. Consider adding context at the dataset or schema level if you notice the AI struggling to locate or understand specific data.
Basedash automatically imports table and column descriptions that are already set up in your BigQuery instance. If you have existing metadata in BigQuery, it will be available in Basedash without additional configuration.
When to add context
Complex transformed data : When the AI needs help understanding data transformation logicBusiness-specific metrics : If calculated fields or KPIs need additional explanationUnclear naming conventions : When table or column names don’t clearly indicate their purpose
For detailed guidance, see our custom context documentation . 
