Unlocking dbt: Design and Deploy Transformations in Your Cloud Data Warehouse

Master the art of data transformation with the second edition of this trusted guide to dbt.
Building on the foundation of the first edition, this updated volume offers a deeper, more comprehensive exploration of dbt's capabilities-whether you're new to the tool or looking to sharpen your skills. It dives into the latest features and techniques, equipping you with the tools to create scalable, maintainable, and production-ready data transformation pipelines.

Unlocking dbt, Second Edition introduces key advancements, including the semantic layer, which allows you to define and manage metrics at scale, and dbt Mesh, empowering organizations to orchestrate decentralized data workflows with confidence. You'll also explore more advanced testing capabilities, expanded CI/CD and deployment strategies, and enhancements in documentation-such as the newly introduced dbt Catalog.

As in the first edition, you'll learn how to harness dbt's power to transform raw data into actionable insights, while incorporating software engineering best practices like code reusability, version control, and automated testing. From configuring projects with the dbt Platform or open source dbt to mastering advanced transformations using SQL and Jinja, this book provides everything you need to tackle real-world challenges effectively.

What You Will Learn

Understand what dbt is and how it is used in the modern data stack

Set up a project using both dbt Cloud and dbt Core

Connect a dbt project to a cloud data warehouse

Build SQL and Python models that are scalable and maintainable

Configure development, testing, and production environments

Capture reusable logic in the form of Jinja macros

Incorporate version control with your data transformation code

Who This Book Is For
Current and aspiring data professionals, including architects, developers, analysts, engineers, data scientists, and consultants who are beginning the journey of using dbt as part of their data pipeline's transformation layer. Readers should have a foundational knowledge of writing basic SQL statements, development best practices, and working with data in an analytical context such as a data warehouse.