Sometimes, when working with complex dbt projects, you could fall into a situation where you want to decouple one complex model's logic into multiple models that the final model can then leverage. This practice can help you decrease the complexity of a single model, and have more intermediate models with a piece of modeling or transformation.

This is a good practice when working with dbt, and the tool allows you to create these intermediate models without materializing them into the database you are using. This way you can leverage them while dbt is executing, but you don't waste the storage space. These models use the ephemeral materialization type.

In this lab, you will create an ephemeral dbt model, and you will then create a final full-refresh model. You will then check that the ephemeral one has not been stored in your database.

Learning Objectives

Upon completion of this lab, you will be able to:

• Understand ephemeral models and how to use them
• Work and create ephemeral dbt models

Intended Audience

• Data engineers with a focus on data modeling and transformations methodologies
• Developers that need to understand different kinds of dbt materialization techniques

Prerequisites

To get the most from this lab, you should have basic knowledge of dbt. To get ready, you can use the following labs:

• Create Your First dbt (Data Build Tool) Project
• Configure a dbt Profile and Define Sources
• Create and Execute Your First dbt Models
• Working With Full-Refresh dbt Models
• Working With Incremental dbt Models