Connect your Google BigQuery data warehouse to Basedash to analyze and visualize your enterprise data.

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Prerequisites

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Connection setup

  1. From your Basedash dashboard, click “Add Data Source”
  2. Select “BigQuery” as your data warehouse
  3. Upload your service account key file
  4. Select or enter your project ID
  5. Choose default dataset (optional)
  6. Click “Test Connection” to verify
  7. Save your connection

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Required permissions

Your service account needs the following IAM roles:
  • roles/bigquery.dataViewer - to read data
  • roles/bigquery.jobUser - to run queries
  • roles/bigquery.resourceViewer - to list projects and datasets

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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

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Performance optimization

  • Use clustered and partitioned tables
  • Materialize commonly used views
  • Create appropriate table statistics
  • Monitor query costs and performance
  • Set up appropriate caching policies

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Troubleshooting

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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.

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Automatic metadata import

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.

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When to add context

  • Complex transformed data: When the AI needs help understanding data transformation logic
  • Business-specific metrics: If calculated fields or KPIs need additional explanation
  • Unclear naming conventions: When table or column names don’t clearly indicate their purpose
For detailed guidance, see our custom context documentation.