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Connect your Snowflake data warehouse to Basedash to analyze and visualize your enterprise data. For buyer guidance, see BI for Snowflake or the comparison of the best Snowflake BI tools.

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Prerequisites

  • A Snowflake account
  • User credentials with appropriate access
  • An RSA key pair assigned to your Snowflake user (recommended)
  • Account identifier (organization-account.region)
  • Warehouse, database, and schema information
Snowflake is phasing out single-factor password authentication. We recommend connecting with key pair authentication. Password authentication is still supported for now.

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Connection setup

  1. From your Basedash dashboard, click “Add Data Source”
  2. Select “Snowflake” as your data warehouse
  3. Set Authentication method to Private key (recommended)
  4. Enter your connection details:
    • Account identifier
    • Username
    • Private key (upload the RSA private key associated with your Snowflake user)
    • Private key passphrase (if your key is encrypted)
    • Warehouse name
    • Database name
    • Schema (optional)
    • Role (optional)
  5. Click “Test Connection” to verify
  6. Save your connection
To generate an RSA key pair and assign the public key to your Snowflake user, follow Snowflake’s key pair authentication guide.

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Connect with a password

If you haven’t set up key pair authentication yet, you can still connect with a password:
  1. Set Authentication method to Password
  2. Enter your username and password along with the other connection details above
Note that Snowflake is phasing out password-only authentication, so plan to migrate to key pair auth.

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Required permissions

Your Snowflake user needs:
  • USAGE on warehouse
  • USAGE on database
  • USAGE on schema
  • SELECT on required tables
  • MONITOR on warehouse (recommended)

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Best practices

  • Create a dedicated user for Basedash
  • Use appropriate warehouse sizing
  • Grant minimum required permissions
  • Set resource monitors for cost control
  • Regularly rotate credentials

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Performance optimization

  • Choose appropriate warehouse size
  • Use clustering keys for large tables
  • Leverage materialized views
  • Set up proper table maintenance
  • Configure query timeouts
  • Enable result caching

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Troubleshooting

  • Verify user permissions
  • Check warehouse availability
  • Monitor credit usage
  • Review query history for errors
  • Validate network access

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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 database or schema level if you notice the AI struggling to locate or understand specific data.

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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.