dbt v2 is here

dbt v2 is here: a new foundation built for speed, rigor, and the agentic era. dbt v2 adds a local compiler for SQL for the first time. Instead of finding out a query is broken only after running it against the warehouse, dbt now emulates your database's behavior and produces a logical plan before you deploy, catching mistakes early and saving time, money, and warehouse compute. It also gives agents a verifiable feedback loop instead of code they can only hope will work. It's fast throughout. Moving to an optimized Rust binary and modern technologies like ADBC means a project that used to take 20 minutes now completes in under one, with everyday projects seeing meaningful speedups too. Your whole project can now be described as standard Parquet files, cutting file size and enabling fast querying of your own project's metadata through tools like DuckDB. That means you can find dead columns, spot models with the least test coverage, enforce naming and ownership conventions as code, or let your agent look up lineage and blast radius instantly. dbt docs also got a full rebuild: a new, scalable experience powered by an embedded DuckDB engine that reads project metadata on demand, so it holds up even on very large projects. dbt v2 integrates seamlessly with dbt State, so you can skip or clone models that don't need to rerun. Get started with the curl-based install for automatic updates, or use pip if you want dbt inside your existing Python virtual environment. Install dbt: https://docs.getdbt.com/docs/local/install-dbt?install-method=curl Learn more about dbt State: https://www.getdbt.com/product/dbt-state #dbt #AnalyticsEngineering #OpenSource #DataEngineering