Status: experiment; feedback and contributions welcome!
Built to solve 3 problems I have with SQL as my primary iterative analysis language:
1. Adjusting queries/analysis takes a lot of boilerplate. Solve with queries that operate on the semantic layer, not tables. Also eliminates the need for CTEs.
2. Sources of truth change all the time. I hate updating reports to reference new tables. Also solved by the semantic layer, since data bindings can be updated without changing dashboards or queries.
3. Getting from SQL to visuals is too much work in many tools; make it as streamlined as possible. Surprise - solve with the semantic layer; add in more expressive typing to get better defaults;also use it to wire up automatic drilldowns/cross filtering.
Supports: bigquery, duckdb, snowflake.
Links [1] https://trilogydata.dev/ (language info)
Git links: [Frontend] https://github.com/trilogy-data/trilogy-studio-core [Language] https://github.com/trilogy-data/pytrilogy
Previously: https://news.ycombinator.com/item?id=44106070 (significant UX/feature reworks since) https://news.ycombinator.com/item?id=42231325
