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Key benefits

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

Description

dbt 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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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JR May 01, 2024

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SF Sep 12, 2024

Has a great walk through of each subject for most people.

Richmond Eweh Aug 16, 2023

This book is by far the best book/resource (apart from tutorial videos) there is on DBT. I'd been waiting or the book to drop months ago!! I even reached out to Roberto just to put the pressure on him hahhaha. And the book lived up to every expectation as expected! It not only covers the ins-outs of dbt itself but also the broad concepts and examples of real-world Data Engineering principles. From modeling, to transforming data, to setting a proper structure for Dimensional data and even writing durable code for standardization like Macros.I'm currently using this book to study for my dbt - Analytics Engineer certification and I'm sure without a doubt that I will pass.

Daniel Eduardo Portugal Revilla Aug 28, 2023

"Data Engineering with dbt" isn't just a book about dbt; it's so much more than that. It delves into the entire ecosystem of technologies surrounding dbt, covering best practices in modeling, writing maintainable code that aligns with software engineering principles applied to the data realm, data transformation techniques, and the application of data quality tests to ensure consistent data delivery to the business. Additionally, it provides an introduction to tools within the Data Modern Stack, such as Snowflake, one of the most popular and robust Data Warehouses of the moment, Git, GitHub, and even using dbt in either its cloud version or the open-source version on our local machines. The book ultimately brings all these components together in an automation process for deployment, execution, and documentation.In essence, this book offers a comprehensive exploration of the data lifecycle, focusing on the transformation stage. It provides a refresher on SQL fundamentals, covers modeling, and guides readers to build robust, high-quality models following best practices.Rating:Clarity and Explanation: 5/5Depth and Coverage: 5/5Practical Examples: 4.7/5Progression and Difficulty: Easy to read / Entry levelPrerequisite Knowledge:- Basic SQL skills

Om S Jul 05, 2023

Beginning with the basics of SQL for data transformation, the book swiftly progresses to setting up the dbt Cloud development environment. Through practical examples and step-by-step instructions, readers learn data modeling techniques, understand the changing role of analytics engineering, and harness dbt to transform and test data accurately. The book emphasizes the importance of maintainable code, working with dimensional data, ensuring consistency and reliability, and implementing agile development practices.With a focus on collaboration, automation, and enhancing software quality, the book equips readers to build data platforms that are agile, scalable, and well-documented. It also provides patterns for frequent use cases, guiding readers through common challenges encountered in data engineering projects. Whether readers are newcomers or experienced professionals, this book offers primers on data-related subjects to help them get started and excel in their roles."Data Engineering with dbt: A Practical Guide to Building a Cloud-Based, Pragmatic, and Dependable Data Platform with SQL" is a must-have guide for those seeking to build efficient and reliable data platforms. The book's clear explanations, practical examples, and emphasis on simplicity and maintainability make it accessible and valuable for individuals and teams alike. With its focus on leveraging dbt and SQL, this book empowers readers to become proficient in data engineering and drive successful data-driven initiatives.

Data Engineering with dbt: A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

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