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Course
- Data
Packages, Macros, and Performance Optimization in dbt
Extend dbt with packages, macros, and performance tuning. This course shows how to automate SQL logic, optimize models with materializations and partitions, and manage costs for scalable, efficient data transformations.
What you'll learn
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.
