Course overview
dbt has become essential for transforming data in modern analytics workflows. In this course, you’ll learn to build transformation pipelines from the ground up—starting with core concepts like models and DAGs, then adding testing, documentation, and incremental processing. You’ll finish with production patterns that make pipelines maintainable and deployable, always with an eye toward knowing when added complexity is justified.
Key skills
- Setting up and organizing dbt projects following established conventions
- Writing dbt models that transform raw data into analytics-ready datasets
- Building reliable pipelines with dependency graphs and data quality tests
- Making informed decisions about when to use incremental models versus simpler approaches
- Deploying dbt projects with proper separation between development and production
Course outline
Data Transformation with dbt [4 lessons]
dbt Fundamentals: Models, DAGs, and the Analytics Engineering Mindset 2h
Lesson Objectives- Set up a dbt project and understand dbt's role in the modern data stack
- Write dbt models using SQL that dbt transforms into database objects
- Implement dbt's ref() and source() functions to build automatic dependency graphs
- Execute dbt commands to run transformations in correct dependency order
- Configure dbt materializations and organize models using dbt conventions
dbt Environments: Testing, Documentation, and Development Workflows 2h
Lesson Objectives- Implement generic tests to validate data quality assumptions
- Apply dbt build to integrate testing into transformation workflow
- Generate interactive documentation with lineage graphs visualizing DAG
- Configure development and production environments using dbt profiles
- Write detailed model descriptions explaining transformations and purpose
dbt Incremental Models: How They Work and When to Use Them 2h
Lesson Objectives- Convert table models to incremental materialization configuration
- Implement conditional filtering with is_incremental() and lookback windows
- Debug SQL dialect differences and type casting issues
- Identify silent failure modes in incremental data processing
- Evaluate performance trade-offs to justify optimization complexity
dbt Production Patterns: Macros, Packages, and Deployment 2h
Lesson Objectives- Recognize when to remove complexity that doesn't provide value
- Use built-in and custom macros to encapsulate reusable logic
- Install dbt packages and leverage battle-tested utility functions
- Implement deduplication using surrogate keys and window functions
- Configure and execute production builds with environment isolation
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