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Connect your ClickHouse data warehouse to Basedash to analyze and visualize your enterprise data.

Prerequisites

  • A ClickHouse server or cloud instance
  • User credentials with appropriate access
  • Server host and port information
  • SSL certificates (if using secure connection)

Connection setup

  1. Open the Data page and click Add data source
  2. Select ClickHouse
  3. Enter your connection details:
    • Database name
    • Username
    • Password
    • Host
    • Port: the HTTP interface port, usually 8443 with SSL (the default, and what ClickHouse Cloud uses) or 8123 without
    • SSL (turn on for HTTPS)
  4. Click Connect. Basedash checks the connection, then opens the new data source and starts syncing its schema.
The ClickHouse connection form in Basedash with database name, username, password, a ClickHouse Cloud host, and port 8443 filled in and SSL turned on

Required permissions

Your ClickHouse user needs:
  • SELECT privilege on required databases/tables
  • SHOW privilege on databases
  • SHOW TABLES privilege
  • SHOW COLUMNS privilege

Best practices

  • Create a dedicated user for Basedash
  • Enable SSL for secure connections
  • Grant minimum required permissions
  • Set appropriate query limits
  • Regularly rotate credentials

Performance optimization

  • Use appropriate table engines
  • Configure proper partitioning
  • Set up materialized views
  • Monitor query performance
  • Use efficient indexing
  • Enable query caching

Troubleshooting

For what each connection error means and how to fix it, see Connection settings.
  • Verify user permissions
  • Check server connectivity
  • Monitor resource usage
  • Review server logs
  • Validate query syntax
  • Check SSL configuration

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.

When to add context

  • Complex analytics data: When the AI needs help understanding data structure and metrics
  • 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.