Trilogy – A Reusable, Composable SQL Experiment
Recipe: Add a semantic layer to SQL; use it drop the requirement for joins/group_by; add in type-checking and a lightweight python-esque import syntax to enable reuse and hierarchical querying. Trilogy is intended to provide an accessible but deep alternative to raw SQL. It offers a new-but-inspired-by-SQL syntax that compiles to various dialects of SQL (with DuckDB as the default). The target audience is people that really like SQL for analytics and data engineering, but want less boilerplate and sharp edges and looser coupling to the DB. Semantic models can be easily shared, composed…
Trilogy – A Reusable, Composable SQL Experiment launched on November 24, 2024 with 78 votes, #462 of 4,990 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Trilogy is a SQL alternative that adds a semantic layer to reduce boilerplate like joins and group-by clauses. It uses a Python-inspired syntax with type-checking and a reusable import system, compiling to various SQL dialects including DuckDB. Designed for analysts and data engineers who prefer SQL but want cleaner code and looser database coupling, Trilogy enables interactive semantic model composition and sharing while maintaining SQL's ad-hoc workflow capabilities. It includes experimental ETL and DBT integration support.
written from the facts on this page · September 2026
From the sources
Simply SQL
This demo uses the popular TPC-DS dataset, often used to benchmark performance for databases. We’ll use it a little differently here. DuckDB can generate a representative data warehouse from TPC-DS using a simple extension, and we’ll use that to explore Trilogy syntax. You can read more about Trilogy and our use of this benchmark here . This demo uses a backing service that may take a few seconds to cold-start; follow-up queries should be fast. This service uses DuckDB. Trilogy is database-agnostic: the same syntax works on any backend, such as Postgres, BigQuery, or Snowflake. TPC-DS provides a nice, tidy warehouse: 17 dimension tables and 7 fact tables. That’s a lot to work with!…from trilogydata.dev
In the maker’s words, at launch
Recipe: Add a semantic layer to SQL; use it drop the requirement for joins/group_by; add in type-checking and a lightweight python-esque import syntax to enable reuse and hierarchical querying. Trilogy is intended to provide an accessible but deep alternative to raw SQL. It offers a new-but-inspired-by-SQL…
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