Nasty, a cross-warehouse, type-checked, unit-testable analytics library
Hey HN - our team wants to open source a project called NASTY (NASTY Abstract Syntax Tree thingY) that we built for ourselves. NASTY was built to maintain testable/composable data pipelines. Our team was ripping our hair out trying to maintain dbt/SQL scripts across different data warehouses (Redshift, BigQuery, Postgres, Snowflake) on top of ever shifting data foundations maintained by our customer's internal data teams. NASTY is the result of our learnings from field experience. We wanted to write abstractions so that we could reuse code. We wanted to bundle those abstractions…
In plain words
Nasty is a cross-warehouse analytics library that enables testable and composable data pipelines across multiple databases including Redshift, BigQuery, Postgres, and Snowflake. It provides type checking and static analysis to catch errors before production, allows unit testing locally without warehouse connections, and supports code reuse through composable abstractions. Built by engineers managing complex dbt and SQL scripts across multiple data warehouses, Nasty applies standard software development practices to data pipeline development.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN - our team wants to open source a project called NASTY (NASTY Abstract Syntax Tree thingY) that we built for ourselves. NASTY was built to maintain testable/composable data pipelines. Our team was ripping our hair out trying to maintain dbt/SQL scripts across different data warehouses (Redshift, BigQuery, Postgres, Snowflake) on top of ever shifting data foundations maintained by our customer's internal data teams. NASTY is the result of our learnings from field experience. We wanted to write abstractions so that we could reuse code. We wanted to bundle those abstractions into libraries. We wanted to statically analyze our models so that we caught more errors before production. We wanted a fast unit test suite that you could iterate on locally without connecting to a data warehouse. In short, we wanted to use all the same practices we used for building our other software. Tools like dbt made a great start at importing these kinds of practices into analytics, but there are many great aspects of the software engineering workflow that still aren't easy to replicate in data. We've found it to be a super productive way to work, and we thought others might want to use it too. It's very early days so we've put up a page for to explain it, and we've made some executable examples for people to have a go. Let us know what you think! FAQ: Is NASTY an ORM? A SQL builder? Neither! NASTY is kinda it's own thing. NASTY is more like a minimal relational algebra programming language shipped as a Typescript library. It borrows a bunch of learnings from other programming languages and applies them to OLAP programming / data engineering. It's an important distinction, because it totally changes the way you can think about building transformations. Why TypeScript? Why not Python? The 100% honest answer is we had a ton of typescript experience and we had a node/react app with pulumi's TS apis. We like typescript and by being a library instead of a full programming language, we can leverage things like vitest, eslint, node, TS's language server, TSdoc, etc.
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