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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering
Key FeaturesDiscover how analytics engineering aligns with your organization's data strategyAccess insights shared by a team of seven industry expertsTackle common analytics engineering problems faced by modern businessesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights.
You’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, Google BigQuery for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll find effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. With invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle.
By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.
What you will learnDesign and implement data pipelines from ingestion to serving dataExplore best practices for data modeling and schema designGain insights into the use of cloud-based analytics platforms and tools for scalable data processingUnderstand the principles of data governance and collaborative codingComprehend data quality management in analytics engineeringGain practical skills in using analytics engineering tools to conquer real-world data challengesWho this book is forThis book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
Table of ContentsWhat is Analytics Engineering?The Modern Data StackData IngestionData WarehousesData ModelingData Transformation Serving Building a Data PlatformData Quality & Observability Writing Code in a TeamWriting Robust Pipelines Gathering Business RequirementsDocumenting Business LogicData Governance
Key FeaturesDiscover how analytics engineering aligns with your organization's data strategyAccess insights shared by a team of seven industry expertsTackle common analytics engineering problems faced by modern businessesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights.
You’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, Google BigQuery for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll find effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. With invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle.
By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.
What you will learnDesign and implement data pipelines from ingestion to serving dataExplore best practices for data modeling and schema designGain insights into the use of cloud-based analytics platforms and tools for scalable data processingUnderstand the principles of data governance and collaborative codingComprehend data quality management in analytics engineeringGain practical skills in using analytics engineering tools to conquer real-world data challengesWho this book is forThis book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
Table of ContentsWhat is Analytics Engineering?The Modern Data StackData IngestionData WarehousesData ModelingData Transformation Serving Building a Data PlatformData Quality & Observability Writing Code in a TeamWriting Robust Pipelines Gathering Business RequirementsDocumenting Business LogicData Governance
559 pages, Kindle Edition
Published March 29, 2024
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Displaying 1 - 3 of 3 reviews
January 2, 2025
Solid book! Good intro to analytics engineering. I liked that it started off with nontechnical components and higher level abstractions as well as embedded the role and priorities of an analytics engineer within the larger org. It introduced technical topics as they related to expounding on what an analytics engineer does. Only at the end of the book does it actually discuss coding and nitty-gritty engineering. It’s a good reminder that the job of an engineer is to solve problems in collaboration with others, and coding is often the last thing that needs to be done, not the first.
February 18, 2026
I found this book to be an excellent and highly practical resource. It’s one of the most comprehensive books I’ve read and I’d recommend to anyone looking to upskill as a data analyst or transition into analytics engineering.
In particular, the data modeling section was incredibly valuable for me. The book not only explains different analytics warehouse modeling approaches, but also walks through the historical evolution behind them. Understanding how and why these modeling patterns developed over time helped me think more critically about how we should design data models and understand the bottlenecks that I experience in modelling.
What I really appreciated is how thoroughly it covers the entire analytics pipeline from data ingestion and transformation to modeling, testing, orchestration and governance. It provides concrete tool recommendations and real-world use cases that make the concepts immediately applicable.
More to that, if you are new in dbt, it would help you understand how dbt works with various tools such as Airflow, Docker, or BI tools.
In particular, the data modeling section was incredibly valuable for me. The book not only explains different analytics warehouse modeling approaches, but also walks through the historical evolution behind them. Understanding how and why these modeling patterns developed over time helped me think more critically about how we should design data models and understand the bottlenecks that I experience in modelling.
What I really appreciated is how thoroughly it covers the entire analytics pipeline from data ingestion and transformation to modeling, testing, orchestration and governance. It provides concrete tool recommendations and real-world use cases that make the concepts immediately applicable.
More to that, if you are new in dbt, it would help you understand how dbt works with various tools such as Airflow, Docker, or BI tools.
August 29, 2025
Great introduction to Analytics Engineering. Definitely worth-reading. Can anyone recommend similar books?
Displaying 1 - 3 of 3 reviews