Название: Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale
Автор: Rui Pedro Machado, Helder Russa
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 324
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL.

Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence.

With this book, you'll learn:

What dbt is and how a dbt project is structured
How dbt fits into the data engineering and analytics worlds
How to collaborate on building data models
The main tools and architectures for building useful, functional data models
How to fit dbt into data warehousing and laking architecture
How to build tests for data transformations

Who This Book Is For:
This book is designed for professionals, students, and enthusiasts dealing with the complex world of data management and analytics. Whether you’re an experienced veteran reminiscing about the basic principles of data modeling or an aspiring analyst keen to understand the transformation from business intelligence to contemporary analytics engineering, our storytelling assures clearness and direction.

Organizations seeking to strengthen their data processes will discover immense value in the combination of well-proven principles and modern tools discussed in this book. In summary, if you wish to take full advantage of your data by combining the strengths of the past with the innovations of the present, this book will guide you.