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Case Study: Building E-Commerce Data Models with dbt
4.8(444)3k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This hands-on case study provides a practical application of dbt (Data Build Tool), the industry-standard SQL-based transformation framework. Designed around a real-world e-commerce dataset, it guides learners through structuring raw transactional data into clean, analysis-ready models.
Who It's For
Early-stage data engineers and analysts seeking advanced, practical experience setting up dbt environments and writing scalable data transformations.
Key Takeaways
- dbt Setup & Modeling: Configure dbt profiles, structure raw data, and construct scalable staging models while debugging syntax and handling project setup.
- Data Quality Checks: Implement rigorous data validation checks to ensure dataset accuracy before delivering data for business analysis.
- Jinja Automation: Write modular, DRY (Don't Repeat Yourself) code using Jinja variables, control flow, and loops to streamline dbt workflows.
About this course
Transform raw data into clean, reliable models using dbt (Data Build Tool) - a modern, SQL-based transformation framework used by data teams around the world. This hands-on case study course is designed for early-stage learners who want to build real-world skills through guided, practical exercises. You'll set up your own dbt environment, model data at scale, and write reusable code using dbt's built-in features.
Set Up Your Project and Explore the Data
Get started by setting up a dbt project and working with a real E-Commerce dataset. You'll structure raw data, configure profiles, and debug syntax issues while gaining insight into the business context behind each transformation step.Build and Validate Models
Learn to create scalable staging models and apply data quality checks to ensure your datasets are accurate and analysis-ready. You'll build a solid foundation for answering key business questions.Automate with Jinja
Finish the course by learning how to use Jinja to write reusable, maintainable code. You'll use variables, control flow, and loops to follow the DRY (Don't Repeat Yourself) principle to streamline your dbt workflow.Advertisement