Getting instant feedback: The new dbt powered developer experience

dbt v1 workflows still rely on a slow feedback loop: write SQL, kick off a run, wait for failures, then dig through logs to understand what broke. With dbt v2, that changes. The same Rust-based engine behind v2 now powers a development experience that understands your project as you work – catching issues in real-time, generating parquet artifacts for fast metadata exploration, and giving you an updated model and column lineage as you or your agent develops in your dbt project.

In this session, we’ll follow an analytics engineer through a full local development loop: authoring a model with SQL comprehension and inline diagnostics, exploring column‑level lineage and catalog to understand your project, using local Compare Changes to validate impact against your production models without waiting for CI, and using a high‑performance, SQLFluff‑compatible linter to enforce consistency and standards.

You’ll leave with a clear picture of what dbt v2 unlocks in terms of development speed and practical guidance on how to introduce these capabilities to your team without disrupting existing projects.