Data Semantics

Data Semantics refers to the metadata and business rules that define the real-world meaning and relationships of database fields. It goes beyond technical definitions (such as column data types) to document what the data represents in business operations.

How it Works

Data semantics attaches context to database fields. In a semantic metadata registry, data elements are annotated with descriptions, metric formulas, and relationship rules. This maps a technical column name (like cust_stat_cd_4) to a clear business concept (like “Active Customer”).

• Business Glossaries: Maps technical columns to human-readable terms, providing semantic context across systems.
• Semantic Mapping: Explains the relationships between different datasets, defining cardinality and data logic.

Lakehouse & Agentic Relevance

In a data lakehouse, data semantics provides clarity over raw files. Without data semantics, raw files in object storage are difficult to interpret. For AI agents, data semantics acts as the context layer that guides LLM understanding. During Text-to-SQL generation or retrieval operations, the agent reads data semantics to understand what tables represent and when to filter records. Dremio supports data semantics by allowing architects to add business descriptions and tags to virtual datasets, creating an open directory for downstream users and agents.