dbt (Data Build Tool)
Data transformation, models, tests, documentation, and CI/CD for modern data pipelines
What You'll Learn
- dbt project setup, directory structure, and configuration
- Develop and test dbt models for data transformation
- Build data validation tests and quality checks
- Generate and maintain data documentation automatically
- Implement Jinja templating for dynamic SQL generation
- Set up CI/CD pipelines and deploy data pipelines in production
- Collaborate with teams using dbt Cloud
- Optimise data pipelines for performance and cost
Course Modules
dbt Fundamentals & Setup
Installation, project structure, profiles, and your first dbt transformation model.
Models, Materialisations, and Lineage
Build views, tables, and incremental models. Understand data lineage and dependencies.
Testing & Data Quality
Implement generic and custom tests to validate data quality and integrity.
Documentation & Macros
Create self-documenting pipelines and write Jinja macros for reusable logic.
dbt Cloud & Orchestration
Deploy models to production, set up scheduled runs, and monitor pipeline health.
Advanced Patterns & Best Practices
Source freshness, snapshot patterns, and production pipeline governance.
Tools & Technologies
Career Relevance
Data Engineer
Essential for building and maintaining scalable data transformation pipelines in production.
Data Scientist
Recommended for understanding data lineage and preprocessing pipelines for ML projects.
Data Analyst
Optional but valuable for understanding data preparation and pipeline monitoring.
Prerequisites
- Strong SQL knowledge (queries, joins, aggregations)
- Basic understanding of data warehousing concepts
- Familiarity with Git and version control basics
- Access to a local development environment (Python, CLI)
Ready to Master dbt?
Get access to this course, plus 13 more professional data & AI courses.