Analytics Engineering with SQL and DBT : Building Meaningful Data Models at Scale

Rui Machado

ISBN 10: 1098142381 / ISBN 13: 9781098142384

Published by O'reilly Media Jan 2024, 2024

About this Item

Neuware - 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.

Seller Inventory # 9781098142384

Bibliographic Details

• Title: Analytics Engineering with SQL and DBT : Building Meaningful Data Models at Scale
• Publisher: O'reilly Media Jan 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 1098142381
• ISBN 13: 9781098142384
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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

About the Authors

Rui Machado is a Director of Data Engineering at Monstarlab and has a background in Information Technologies and Data Science. Has over a decade of relevant experience in the architecture and implementation of data warehouses, data lakes, and decision support systems in industries such as Retail, Ecommerce, Supply Chain, Healthcare, and Social Networks. Has led Engineering and Analytics teams at Jumia, Nike, and Facebook. He is also co-founder and CEO of ShopAI.co. He has previously collaborated with Synfusion in publishing three technical books on Powershell, SSIS, and BizTalk Server.

HR. Helder Russa is a Data Engineering Lead at Jumia with a background in Information Technologies and Data Science. Has over 10 years of professional experience in computer science, with an emphasis on evolving and maintaining data solutions applied to decision making. Nowadays, he works as a lead data engineer at Jumia where he contributes to the strategy definition, design, and implementation of multiple Jumia data platforms. In similitude, and since 2018, he is a co-founder and data architect of ShopAI, a company specialized in deep learning, that leverages the capabilities of the image for optimization of search channels inside webshops. LinkedIn profile: https://www.linkedin.com/in/hrussa/