Data Engineering With Dbt
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Data Engineering with dbt
Author: Roberto Zagni
language: en
Publisher: Packt Publishing Ltd
Release Date: 2023-06-30
Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started.
Analytics Engineering with SQL and Dbt
Author: Rui Pedro Machado
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-12-08
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
dbt for Analytics Engineering
"dbt for Analytics Engineering" "dbt for Analytics Engineering" is a comprehensive guide for modern data practitioners seeking to master the evolving discipline of analytics engineering. The book begins by tracing the origins of analytics engineering and examining the emergence of the modern data stack, with an in-depth look at dbt’s transformative role in shaping data workflows, architectural patterns, and large-scale organizational adoption. Through real-world case studies and expert insights, readers will gain a foundational understanding of how dbt enables efficient, collaborative, and scalable data transformation practices within diverse business contexts. Diving into advanced project architecture, the book offers practical frameworks for structuring scalable dbt projects, managing configurations across multiple environments, and implementing robust model materializations. Readers will learn to harness Jinja and macros for code reusability, ensure high-performance data modeling using dimensional and Data Vault approaches, and adopt modular design patterns that optimize both maintainability and analytical clarity. In addition, dedicated chapters address the rigorous testing, quality assurance, and data governance practices needed to ensure trust, compliance, and discoverability in enterprise data assets. The practical reach of "dbt for Analytics Engineering" extends to cloud warehouse optimization, orchestration, automation, and CI/CD integration, providing readers with strategies for deploying and managing analytics projects at enterprise scale. The book concludes by exploring the technological frontiers of analytics engineering—from integrating machine learning and real-time data streaming to building custom dbt plugins and embracing federated data models. With actionable guidance on scaling analytics teams, managing dependencies, and implementing secure, audit-ready workflows, this book is an indispensable resource for anyone seeking to lead or innovate in the era of modern analytics engineering.