DBT Production Setup : Data Modeling, Automation, CI/CD & Cost Optimization
Master the complete analytics engineering workflow by building a production-ready data stack from scratch using DBT (Data Build Tool), the industry-standard transformation framework trusted by data teams worldwide.
What you’ll learn
- Build a production-ready DBT project and understanding everything about DBT set up.
- Having best practices about data modeling and SQL code convention.
- How to automate everything that is too time consuming : testing, documentation and cleaning.
- Monitor and optimize data warehouse costs.
Course Content
- The Modern Data Stack: From Business Need to Command-Line Execution –> 3 lectures • 12min.
- How to set up your DBT project –> 3 lectures • 14min.
- Setting Up Git for Collaborative DBT Development –> 3 lectures • 6min.
- Environment Management with UV –> 3 lectures • 5min.
- How DBT Compilation and Packages Work –> 2 lectures • 7min.
- Data Modeling Layers: Staging, Intermediate, and Mart –> 8 lectures • 38min.
- Code Quality and Documentation: Automation and Best Practices –> 6 lectures • 31min.
- Production Deployment with DBT Cloud –> 6 lectures • 19min.
- Workflow Automation: Makefiles and GitHub Actions –> 4 lectures • 19min.
- Query Cost Monitoring and Attribution –> 3 lectures • 19min.
- Incremental Models: Optimization, Cost Control, and Schema Management –> 3 lectures • 24min.
- Data Quality Testing: From Basic Validation to Automated Fix –> 3 lectures • 18min.
- More DBT Features: Seeds, Macros, and Snapshots –> 3 lectures • 23min.
- Conclusion: My Secrets –> 1 lecture • 8min.
Requirements
Master the complete analytics engineering workflow by building a production-ready data stack from scratch using DBT (Data Build Tool), the industry-standard transformation framework trusted by data teams worldwide.
This comprehensive course takes you from zero to advanced DBT practitioner, covering everything needed to build, deploy, and maintain scalable data pipelines in real-world production environments. You’ll learn the exact methodologies and best practices I’ve developed over 12+ years working across data analyst, data scientist, and analytics engineer roles in fast-growing startups.
What you’ll build:
- Complete three-layer data architecture (staging, intermediate, mart) following software engineering principles
- Automated CI/CD pipelines with DBT Cloud for pull request testing and production deployments
- Cost monitoring system to track and optimize data warehouse expenses
- Self-healing testing framework with automated failure remediation
- Production-grade incremental models for efficient data processing
Key topics covered:
- DBT project setup with development/production environment separation
- Granularity-based data modeling that scales from thousands to billions of rows
- Version control workflows with Git and automated quality enforcement via pre-commit hooks
- SQL linting with SQLFluff and automated documentation generation
- Workflow automation using Makefiles and GitHub Actions
- Query cost attribution and optimization strategies
- Advanced DBT features: seeds, macros, snapshots, and custom tests
Who this is for: Data analysts transitioning to analytics engineering, data engineers building transformation layers, or anyone responsible for maintaining data pipelines serving hundreds of employees and millions of rows.
By the end, you’ll have a battle-tested, production-ready data stack that actually works at scale—not just theory, but proven practices from real company environments.