dbt comes in two flavors: an open-source CLI tool and a commercial managed platform. The choice between them shapes how your team develops, deploys, schedules, and collaborates on data transformations.
Core Notes
These notes break apart the comparison into standalone concepts:
- dbt Core Open-Source Fundamentals — What dbt Core is, CLI workflow, open-source ecosystem, version control integration, and the technical profile of teams that choose it.
- dbt Cloud Managed Platform — What dbt Cloud adds: web IDE, job scheduling, collaboration tools, managed infrastructure, Cloud-exclusive features, and pricing.
- dbt Core vs Cloud Decision Framework — Structured comparison across deployment, interface, features, pricing, and team profile. Decision heuristics for choosing between them.
Related Garden Notes
Deployment and orchestration of dbt Core:
- Cloud Run Jobs for dbt — The default dbt execution environment for cost-conscious GCP teams
- dbt Docker Containerization — Patterns for containerizing dbt Core for production
- dbt Orchestration Decision Framework for GCP — Choosing between Cloud Run, Workflows, and Composer
dbt Cloud in context:
- Dagster vs dbt Cloud Orchestration — When Dagster’s orchestration is worth the setup cost over dbt Cloud’s built-in scheduler
- dbt-Fivetran Merger and the 2026 Transformation Landscape — How the merger is widening the Core/Cloud gap
Broader decision context:
- Build vs. Buy Data Pipeline Economics — The three shifts that flipped build-vs-buy calculations
- dbt Project Structure and Naming — Essential for teams going the Core route
- dbt Testing Taxonomy — Testing works identically in both Core and Cloud