While SQL was standardized by ANSI in 1986, every major database engine and query platform implements its own dialect, with extensions and variations in syntax, functions, and behavior. In a multi-engine lakehouse environment where Spark SQL, Dremio SQL, Trino SQL, and DuckDB SQL all query the same Iceberg tables, SQL dialect differences become a practical engineering challenge.
Common Dialect Differences
The ANSI SQL standard covers the core of SELECT, JOIN, GROUP BY, and window functions, but significant variation exists in:
- Date and Time Functions:
DATE_TRUNC,DATEADD,DATEDIFF, and interval syntax vary widely between Spark, Trino, Dremio, and DuckDB. - String Functions:
REGEXP_EXTRACT,SPLIT_PART, and string concatenation operators differ between dialects. - Array and Struct Types: Iceberg supports nested types (arrays, maps, structs), but the SQL syntax for accessing and manipulating them varies significantly per engine.
- Window Function Syntax: Most engines follow ANSI, but handling of NULLs in window frames and specific frame boundary syntax varies.
Impact on AI Agents
SQL dialect awareness is critical for AI-powered Text-to-SQL systems. An AI agent generating SQL for Dremio must produce Dremio-compatible syntax, not Spark SQL syntax. The Dremio Semantic Layer addresses this by providing a standardized, engine-aware SQL surface that generates correct queries for the target engine, regardless of how the AI agent phrases its request. This means analysts and AI agents can use natural language without needing to know which SQL dialect the underlying engine requires.
