Key features are: Reusability - semantic layer supports full reuse of calculations, CTEs, and even entire models through a lightweight python inspired import syntax. Simple - writing queries that combines data at different granularity is straightforward and avoids common error patterns. Easy to refactor - changing the way the semantic layer binds to the database doesn’t require you to rewrite your queries. You don’t need to codemod 100 ETL scripts to deprecate a table!

Related packages [basic, links available from https://preqldata.dev/]: pypreql-etl -> basic DBT integration. pypreql-nlp -> obligatory GenAI integration. trilogy-studio -> electron IDE for running queries directly. trilogy-public-models -> common repository of public models (mostly BQ public datasets right now) that can be imported/queried.

Differs from other approaches to make a modern SQL I’m aware of (links below) in that it attempts to embrace current SQL syntax, not replace it. Each of these linked projects has a distinct value proposition and is worth checking out on their own!

Note: as you can see by names in the space, it turns out that I was not nearly as creative as I hoped with ‘PreQL’ as a ‘prequel/sequel’ play; PreQL/Trilogy is the intermediate Star Wars pun and it may migrate to just Trilogy (after the sequel!) to avoid confusion.

Other SQL Replacements:

Malloy (full rewrite, semantic focus) https://news.ycombinator.com/item?id=30053860

PRQL (pipelined SQL alternative, all new syntax) https://news.ycombinator.com/item?id=36866861

preql (much more ambitious, all new syntax) https://news.ycombinator.com/item?id=26447070

Things to do: - Define/implement better null handling - Rewrite some parsing/CLI in rust - More functions/backends, more window flexibility - Performance hooks/optimization