Semantic consistency preserves the same meanings, entities, measures, and business definitions across data-access tools and interfaces. In a lakehouse, it lets teams use preferred engines without sacrificing governance, consistency, or performance. section 9.0 section 4.3.4
Implement semantic consistency by:
- Exposing a unified semantic model across federated sources. section 4.3.4
- Maintaining consistent metric definitions across Iceberg and external datasets. section 4.3.4
- Writing consumption requirements around required tools and interfaces, rather than only current tooling. section 4.3.5
Cross-engine analytics
A platform serving Dremio, Trino, and Snowflake should preserve shared dataset meanings and metric definitions instead of forcing teams to duplicate data for each tool. section 4.3.5 section 4.3.4
