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Analytics Engineering with SQL and dbt: Building Meaningful Data Models at Scale
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 Helder 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
321 pages, Paperback
Published January 16, 2024
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Displaying 1 - 2 of 2 reviews
August 21, 2024
A solid handbook on the emerging field of analytics engineering, which bridges the gap between data engineering and data analytics.
That is why it specifically emphasizes the use of SQL, the lingua franca of DBs, and DBT (data build tool) to create scalable, maintainable, and meaningful data models that can power business intelligence (BI) and analytics workflows.
I would have hoped for a more advanced textbook, as in most of the initial chapters encompass basic Analysts tools and concepts such as Data Modeling and SQL.
Since Analytics Engineer is a more technical and advanced role compared to Data Analyst it would have made sense to skip the basics and go straight into the action.
Although I understand the need to attract as much audience as possible, as a Senior Analytics Engineer myself, I've found the first part of the book redundant and the last one insightful.
That is to say that for a Data Analyst it might be the opposite, making this book not super relevant.
DBT is an open-source tool that enables analytics engineers to transform data in their warehouse by writing modular SQL queries, testing data quality, and documenting data transformations. dbt automates the process of building and maintaining data models, making it easier to manage complex data pipelines.
I've found particularly interesting the sections about DBT macros and the use of Jinja SQL.
One final note: the book focuses almost entirely on the DBT cloud distribution which is a paid service.
I'd have liked to have a more deeper discussion on the open source distribution, DBT Core so to understand how DBT works under the hood.
In fact the book mainly shows the UI without going into the technical details of the CLI.
A bit light on the DBT core part, which is the open source distribution
That is why it specifically emphasizes the use of SQL, the lingua franca of DBs, and DBT (data build tool) to create scalable, maintainable, and meaningful data models that can power business intelligence (BI) and analytics workflows.
I would have hoped for a more advanced textbook, as in most of the initial chapters encompass basic Analysts tools and concepts such as Data Modeling and SQL.
Since Analytics Engineer is a more technical and advanced role compared to Data Analyst it would have made sense to skip the basics and go straight into the action.
Although I understand the need to attract as much audience as possible, as a Senior Analytics Engineer myself, I've found the first part of the book redundant and the last one insightful.
That is to say that for a Data Analyst it might be the opposite, making this book not super relevant.
DBT is an open-source tool that enables analytics engineers to transform data in their warehouse by writing modular SQL queries, testing data quality, and documenting data transformations. dbt automates the process of building and maintaining data models, making it easier to manage complex data pipelines.
I've found particularly interesting the sections about DBT macros and the use of Jinja SQL.
One final note: the book focuses almost entirely on the DBT cloud distribution which is a paid service.
I'd have liked to have a more deeper discussion on the open source distribution, DBT Core so to understand how DBT works under the hood.
In fact the book mainly shows the UI without going into the technical details of the CLI.
A bit light on the DBT core part, which is the open source distribution
March 8, 2025
Analytics Engineering with SQL and dbt serves as a solid primer on dbt and its role in modern data analytics workflows. It effectively introduces key concepts, guiding readers through the fundamentals of using dbt for transforming and organizing data models.
As a software engineer with a decent amount of database experience, I found the book informative but less exciting. The way dbt structures data transformations feels like the equivalent of modern C-style macros, relying on Python and Jinja to generate SQL. Rather than providing a true abstraction, it mostly shifts SQL logic around into code snippets, requiring a significant cognitive load to understand how everything stitches together. The end result often resembles “Fancy SQL” with organizational conventions but also a fair amount of spaghetti code.
That said, I do appreciate how the data engineering field is embracing software engineering best practices, such as version control, modularity, separation of concerns, and concepts from domain driven design. While dbt’s approach feels somewhat hodgepodge at this stage, I’m optimistic that these workflows will mature over time. For those new to analytics engineering, this book is a great starting point although getting up and running is a bit kludgy (even when streamlined). For seasoned software engineers, it provides insight into how the data world is evolving—but might leave you wishing for a more robust abstraction layer and a simpler set of tools.
The biggest takeaway for me was gaining familiarity with the tools, workflows, and mindset of the data engineers I work with. This has made conversations more productive, helped me navigate their chosen tools effectively, and fostered stronger collaboration across our cross-functional teams.
As a software engineer with a decent amount of database experience, I found the book informative but less exciting. The way dbt structures data transformations feels like the equivalent of modern C-style macros, relying on Python and Jinja to generate SQL. Rather than providing a true abstraction, it mostly shifts SQL logic around into code snippets, requiring a significant cognitive load to understand how everything stitches together. The end result often resembles “Fancy SQL” with organizational conventions but also a fair amount of spaghetti code.
That said, I do appreciate how the data engineering field is embracing software engineering best practices, such as version control, modularity, separation of concerns, and concepts from domain driven design. While dbt’s approach feels somewhat hodgepodge at this stage, I’m optimistic that these workflows will mature over time. For those new to analytics engineering, this book is a great starting point although getting up and running is a bit kludgy (even when streamlined). For seasoned software engineers, it provides insight into how the data world is evolving—but might leave you wishing for a more robust abstraction layer and a simpler set of tools.
The biggest takeaway for me was gaining familiarity with the tools, workflows, and mindset of the data engineers I work with. This has made conversations more productive, helped me navigate their chosen tools effectively, and fostered stronger collaboration across our cross-functional teams.
Displaying 1 - 2 of 2 reviews