Data Build Tool (dbt) is a powerful tool that allows data analysts and data engineers to easily and efficiently perform data modeling and transformations. It allows you to define sources you want to start from, and the different transformations you want to apply on your data. All the transformations are dbt models and are SQL select statements. dbt also allows you to define the schema of the models you define and offers you the possibility to perform data quality by testing your data.

The playground is a safe and secure sandbox environment for you to explore your own ideas, follow along with Cloud Academy courses, or answer your own questions all without the need to install any software on your local machine. The extended duration of this playground provides you with ample time to play to your heart's content. The goal is to be able to experiment and learn with little start time or overhead. Feel free to experiment working with dbt by defining sources, models, schema definitions, and tests. You will have access to the dbt IDE where you can perform all the operations from.

Intended Audience

This lab is intended for:

• Data analysts that need to model data to extract useful insights
• Data engineers that need to build up ELT pipelines

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
• Working With Ephemeral dbt Models
• Understand and Use dbt Jinja Macros
• Understand and Work With dbt Seeds
• Defining and Working With dbt Tests
• Best Practices for Organizing dbt Models