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🛡️ Prevent invalid metric breakdowns in multi-fact schemas

In complex data models, users can create metric breakdowns that are technically valid but analytically wrong.

These queries run successfully and return plausible numbers, but the results are meaningless. This silent failure undermines trust and hurts self-service.

Now you can control which dimensions can be used with each metric using the filter_direction property on relationships.

Why this matters​

  • Prevents silent data correctness issues before they reach end users
  • Turns semantic correctness into a default behavior, not a best practice
  • Keeps self-service powerful and safe, even in multi-fact (galaxy) schemas
  • Reduces the need for manual user education or tribal knowledge

📚 Learn more​