Course · Intermediate
Data Transformation with dbt
Raw data rarely arrives in a format ready for analysis. dbt has become the standard tool for transforming data within modern data warehouses, letting analysts and engineers write modular SQL that's version-controlled, tested, and documented. But knowing SQL isn't enough—you need to understand how dbt organizes transformations, manages dependencies, and supports the workflows that make data pipelines reliable. This course takes you from your first dbt model through production-ready patterns, building the skills to create maintainable data transformation pipelines.
- Intermediate friendly
- 8 hrs
- 4 lessons
- Premium
Course overview
Learn to transform raw data into analytics-ready datasets using dbt, from foundational concepts through production-ready patterns including testing, documentation, and deployment workflows.
What's inside
4 lessons
- 01 120 mindbt Fundamentals: Models, DAGs, and the Analytics Engineering Mindset
Learn to transform messy real-world data into analytics-ready datasets using dbt. Build a two-layer pipeline with staging and mart models, understand automatic dependency management, and master the analytics engineering mindset with practical examples.
- 02 120 mindbt Environments: Testing, Documentation, and Development Workflows
Learn to add data quality tests, generate interactive documentation, and configure dev/prod environments in dbt. Implement generic tests (unique, not_null, relationships), use dbt build for fail-fast testing, create lineage graphs, and separate development from production.
- 03 120 mindbt Incremental Models: How They Work and When to Use Them
Learn to implement dbt incremental models with lookback windows, understand late-arriving data challenges, debug SQL dialect issues, and critically evaluate when optimization complexity is justified versus using simpler table materializations.
- 04 120 mindbt Production Patterns: Macros, Packages, and Deployment
Learn dbt production patterns: macros for reusable logic, dbt_utils package for testing, deduplication with surrogate keys and window functions, environment isolation for dev/prod deployments, and when to apply these patterns vs. keeping pipelines simple.
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