dbt for Data Engineers

Introduction to dbt

Before the practice, you will learn:

• The difference between ETL and ELT,
• The challenges faced by modern pipelines,
• How dbt Core and dbt Cloud differ and their key advantages.

Setup: Snowflake, dbt Core, and GitHub

For the practice, you will:

• Create a repository on GitHub,
• Create an account in dbt Cloud and set up a data warehouse in Snowflake,
• Perform basic configuration of the project in dbt and define the model structure (SQL or Python file).

Building Data Pipelines in dbt

You will create a chain of models (pipelines) using an e-commerce dataset. You will use dbt Core, dbt Cloud, and Snowflake for step-by-step data transformation.

Materializations in dbt

After building the models, you will learn how to save transformation results:

• In tables,
• Views,
• Incremental or ephemeral models.

You will also learn how external and internal dbt sources work and the dependencies between them.

Testing dbt Models

You will learn how to test models - a key part of reliable data work:

• Generic and bespoke tests,
• Quality and consistency checks of data at all pipeline stages.

Deployment and Scheduling Models

Now that models are working locally, you will learn how to:

• Share them with the team,
• Run them on a schedule,
• Update models automatically.

You will explore practices for deployment and scheduling in dbt Cloud.

Advanced dbt Features

At the end of the course:

• Set up CI/CD processes directly in dbt Cloud,
• Generate complete project documentation and understand how to use it within a team,
• Learn about best practices for working with dbt in production.

What the Course Includes

• Source code repository (GitHub)
• E-commerce dataset
• Step-by-step video tutorials
• A selection of useful links and additional materials

Requirements

• Basic knowledge of relational databases
• Ability to work with SQL
• Recommended: basic experience with Git and cloud platforms (Snowflake, dbt Cloud)