Rebuilding dbt Core in the open: Faster runtime, adapters, and docs v2

The dbt Core v1.x era is long loved through its 9 year tenure and development, but more recently has left teams juggling a Python runtime that slowed down on big projects, fragmented adapters, and a docs experience that struggled to keep up (how big is your manifest.json). dbt Core v2.0 rebuilds that open source foundation: a Rust-based, Apache 2 engine shared with the Fusion experience, a cleaner adapter model built on ADBC and the Arrow ecosystem, and new parquet-backed artifacts that power a faster, more navigable docs experience on large projects.  

In this session, we’ll unpack what the new OSS model actually means in practice: how adapters move into the shared engine and contribution path, what changes (and doesn’t) for community maintainers, and how to take a project from Core v1.x – via the v1.12 parser on‑ramp – to v2.0 with confidence. You’ll see side‑by‑side parse speed demos, a docs tour on a real‑world project, and a sneak peek at how the same open source runtime becomes the starting point for richer dbt experiences when you choose to install the dbt distribution. 

You’ll leave knowing when and how to move your own projects, what to expect from adapter readiness, and how Core v2.0 sets you up for richer dbt experiences, all while staying firmly in the open source world.