An extension of SQL that allows querying data using business concepts rather than raw table structures.
Semantic SQL is an emerging paradigm that extends standard SQL with semantic awareness — allowing users and AI systems to query data using business concepts, metrics, and relationships defined in a semantic layer, rather than requiring knowledge of underlying table names, join conditions, and column definitions. A Semantic SQL query might look like 'SELECT revenue, active_customers FROM last_quarter' where 'revenue' and 'active_customers' are semantically defined metrics, not raw columns.
Semantic SQL has emerged as the critical bridge between AI language models and enterprise data. When an AI agent needs to answer a business question with data, it must generate SQL. Without semantic SQL, the agent must understand the entire database schema — a brittle, error-prone approach. With semantic SQL, the agent queries against a stable, business-meaningful interface that abstracts away the underlying complexity.
Semantic SQL systems maintain a semantic model that maps business concepts to underlying data structures. When a Semantic SQL query is submitted, it is first resolved against the semantic model to determine the correct tables, joins, and calculations, then translated into standard SQL for execution. This translation layer is where business logic lives — ensuring that every query uses the correct, governance-approved definitions.
A data analyst writes: 'SELECT product_category, revenue, yoy_growth FROM this_quarter WHERE region = North America ORDER BY revenue DESC LIMIT 10.' The Semantic SQL engine resolves 'revenue' to the finance-approved calculation, 'yoy_growth' to the correct year-over-year formula, and 'this_quarter' to the current fiscal quarter — generating the correct underlying SQL automatically.