Packages, Macros, and Performance Optimization in dbt

Overview

Maintaining scalable and cost-efficient dbt projects becomes challenging as data teams grow. Without reusable logic and performance strategies, SQL models become repetitive, inefficient, and expensive to run.

In this course, Packages, Macros, and Performance Optimization in dbt, you’ll gain the ability to extend dbt projects with reusable packages, automate SQL logic with advanced macros, and optimize models for performance and cost efficiency. First, you’ll explore how to enhance your dbt workflow by installing and using community packages like dbt-utils and even creating your own reusable packages.

Next, you’ll discover how to write parameterized and recursive macros using Jinja, and apply loops, conditionals, and logging for scalable automation. Finally, you’ll learn how to optimize dbt models using materializations, partitioning, and clustering—while tracking costs and improving warehouse efficiency.

When you’re finished with this course, you’ll have the skills and knowledge of advanced dbt modeling needed to build scalable, automated, and cost-conscious data transformation pipelines.

Syllabus

• Introduction to dbt Packages and Macros
Overview of dbt and its role in data transformation
Importance of scalability and efficiency in dbt projects
• Enhancing Workflow with dbt Packages
Introduction to dbt-utils and other community packages
Installing and managing dbt packages
Creating custom reusable packages
Best practices for package management
• Automating SQL Logic with Advanced Macros
Introduction to Jinja and its role in dbt
Writing parameterized macros
Creating recursive macros for complex transformations
Utilizing loops and conditionals in macros
Implementing logging and error handling in macros
Best practices for macro development
• Performance Optimization Strategies
Understanding dbt materializations and their impact on performance
Implementing partitioning and clustering for improved query efficiency
Analyzing and optimizing cost efficiency
Monitoring and controlling data warehouse costs
• Case Studies and Practical Applications
Real-world examples of reusable packages and macros
Performance optimization scenarios
Cost analysis and decision-making
• Final Project and Assessment
Applying course concepts to a comprehensive project
Peer reviews and collaborative feedback
Reviewing and discussing challenges and solutions
• Conclusion and Next Steps
Recap of key learnings and skills
Resources for further study and community involvement
Planning for future enhancements in your dbt projects

Taught by

Pinal Dave

Subjects

Business