Version Control, CI/CD, and Deployment in dbt
dbt deployments fail when version control and CI/CD aren’t locked down—and you’re losing hours to manual processes. This 51-minute course cuts through the noise, teaching you how to automate dbt workflows, enforce code quality gates, and deploy with confidence. Stop treating deployment as an afterthought.
AIU.ac Verdict: Ideal for analytics engineers and data platform teams who need production-grade dbt workflows without the guesswork. The course moves fast and assumes dbt familiarity; if you’re brand new to dbt itself, front-load some foundational learning first.
What This Course Covers
You’ll work through Git-based version control strategies tailored to dbt projects, including branching patterns that prevent merge conflicts in analytics code. The course then layers in CI/CD pipeline design—automated testing, linting, and schema validation before code hits production. Expect hands-on labs where you configure deployment triggers, manage environment promotion (dev → staging → prod), and troubleshoot common pipeline failures.
The practical focus covers real-world scenarios: rolling back failed deployments, managing secrets and credentials safely, and scaling CI/CD across multiple dbt projects. Maryam walks you through orchestration patterns and shows how to integrate dbt with cloud platforms, so you leave with a deployment blueprint you can adapt to your stack immediately.
Who Is This Course For?
Ideal for:
- Analytics Engineers: Need to move dbt projects from local development to reliable production pipelines with version control and automated testing.
- Data Platform Engineers: Building or scaling dbt infrastructure across teams and want to enforce consistency, prevent breaking changes, and automate deployments.
- DevOps/Platform Teams Supporting Analytics: Managing CI/CD for data teams and need to understand dbt-specific deployment challenges, rollback strategies, and environment management.
May not suit:
- dbt Beginners: This assumes you’re comfortable with dbt fundamentals (models, tests, documentation). Start with dbt essentials first.
- Infrastructure-Only Roles: If you’re not involved in dbt project decisions or deployment ownership, the course may feel too domain-specific.
Frequently Asked Questions
How long does Version Control, CI/CD, and Deployment in dbt take?
51 minutes of video content. Plan 2–3 hours total if you’re working through the hands-on labs and applying concepts to your own dbt project.
Do I need dbt experience before starting?
Yes. You should be comfortable with dbt models, tests, and basic project structure. This course assumes you’re past ‘what is dbt?’ and focuses on production workflows.
What cloud platforms does this cover?
The course teaches CI/CD and deployment principles applicable across platforms. Expect examples with major cloud data warehouses, but the patterns transfer to your stack.
Will I learn how to set up GitHub Actions or GitLab CI?
The course covers CI/CD strategy and dbt-specific pipeline design. You’ll see orchestration patterns and deployment triggers; specific tool setup depends on your platform choice.
Course by Maryam Zakeryfar on Pluralsight. Duration: 0h 51m. Last verified by AIU.ac: March 2026.
