Dbt (data build tool) brings software engineering to your data work. Version control for your SQL. Tests for your transformations. Documentation that stays current. Dependency management between your queries.

You write SQL SELECT statements, dbt handles the CREATE TABLE/VIEW, dependency ordering, testing, and documentation. It's 'what if we treated SQL like real code instead of throwaway scripts.'

massive adoption in data teams. dbt is the standard tool for the 'analytics engineering' role that barely existed five years ago.

The catch: the open source core (dbt-core) vs the cloud platform (dbt Cloud) split is where it gets complicated. dbt-core is free and powerful. dbt Cloud adds a UI, scheduling, CI, and the IDE, and that's where dbt Labs makes money. Also: dbt is SQL-only. If your transformations need Python logic, you're adding complexity.

**Free (dbt-core):** Full transformation engine, CLI, testing, documentation generation, packages. Run it anywhere: your laptop, CI/CD, Airflow, any orchestrator. Apache 2.0 (recently relicensed, check current terms).

**dbt Cloud:** - Developer: free for 1 user, IDE, scheduling, basic features - Team: $100/mo per seat, CI/CD, job scheduling, environment management - Enterprise: custom pricing, SSO, audit logs, multi-project, support

The split is clear: dbt-core is the engine, dbt Cloud is the management plane. A data engineer comfortable with the CLI and cron jobs can run dbt-core in production for free indefinitely. dbt Cloud's value is the IDE, scheduling UI, and team collaboration features.

At $100/seat/mo for a 5-person data team, that's $6,000/yr. Compare that to the ops time of running your own dbt orchestration. If your team spends more than 2 hours/mo on dbt ops, Cloud pays for itself.

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