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Building Medallion Architectures: Designing with Delta Lake and Spark
394 pages, Paperback
Published May 6, 2025
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28 people want to read
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Displaying 1 of 1 review
May 9, 2025
It is safe to say that the Medallion Architecture and related topics in this book are bread and butter of the daily work of our team: We work on the data architecture, engineering, governance, and reporting aspects of various business use cases in a large company. When you have plants all around the world, encompassing a wide variety of data sets and technologies, impacting the work of more than 100.000 employees in one way or another, designing data and information solutions that are flexible and maintainable in the long run is always a challenge.
Therefore, as a Data & Reporting Architect, I’m always in search of finding better ways to do our work, and I couldn’t ask for a better resource than "Building Medallion Architectures" whose very experienced author is not an unfamiliar name for practitioners in the field, especially if you’ve already read his previous book Data Management at Scale: Best Practices for Enterprise Architecture.
If I had to summarize the primary reasons that motivated me to dive into this great resource, I’d say:
- See whether what we currently do and plan to do are aligned with the best practices in the industry.
- Get inspired for potential solutions for current and upcoming business use cases, not only with respect to technological, but maybe even more importantly, to organizational and architectural aspects.
- Learn from other experts that face similar challenges in large and heterogeneous enterprise data landscapes.
This excellent book covers a lot of ground, and I’m satisfied that it starts from the fundamentals and as it progresses towards architectural designs to handle complex data processing scenarios, everything is exemplified concretely. Even though the technological focus of the author is Microsoft Fabric in this book, the designs are pretty much applicable to another technology stack, including but not limited to Azure Databricks, which, by the way is the choice of our company for many use cases. Therefore, I’m thankful to the author because he went the extra mile to concretely demonstrate alternative solutions such as Databricks in his personal blog and gave a link to that in the book. To exemplify this, very recently we did an exercise to structure and govern the Unity Catalog in our Azure Databricks environment, and the solution suggested in the book maps very well to our design!
Having said that, I must also add that this is not a total beginner’s book; to appreciate the deep discussions in the book, you must have already encountered some challenges in large enterprise data settings, and worked with different teams and stakeholders from different business departments.
It is of course not possible to dive into each technical topic at great length; for example, maybe the data quality related sections could be more detailed, but this would make the book much longer. Luckily there are other recent books, such as Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning if you want to learn more.
Conclusion: I can easily recommend this book to experienced data engineers, data architects, and business intelligence architects. Even if you might not agree with all of the principles and design choices, I can guarantee that you’ll find yourself thinking and discussing about them in one way or another within your team and various stakeholders in your company, and having a solid resource such as this book will help you to inform and guide such discussions.
Therefore, as a Data & Reporting Architect, I’m always in search of finding better ways to do our work, and I couldn’t ask for a better resource than "Building Medallion Architectures" whose very experienced author is not an unfamiliar name for practitioners in the field, especially if you’ve already read his previous book Data Management at Scale: Best Practices for Enterprise Architecture.
If I had to summarize the primary reasons that motivated me to dive into this great resource, I’d say:
- See whether what we currently do and plan to do are aligned with the best practices in the industry.
- Get inspired for potential solutions for current and upcoming business use cases, not only with respect to technological, but maybe even more importantly, to organizational and architectural aspects.
- Learn from other experts that face similar challenges in large and heterogeneous enterprise data landscapes.
This excellent book covers a lot of ground, and I’m satisfied that it starts from the fundamentals and as it progresses towards architectural designs to handle complex data processing scenarios, everything is exemplified concretely. Even though the technological focus of the author is Microsoft Fabric in this book, the designs are pretty much applicable to another technology stack, including but not limited to Azure Databricks, which, by the way is the choice of our company for many use cases. Therefore, I’m thankful to the author because he went the extra mile to concretely demonstrate alternative solutions such as Databricks in his personal blog and gave a link to that in the book. To exemplify this, very recently we did an exercise to structure and govern the Unity Catalog in our Azure Databricks environment, and the solution suggested in the book maps very well to our design!
Having said that, I must also add that this is not a total beginner’s book; to appreciate the deep discussions in the book, you must have already encountered some challenges in large enterprise data settings, and worked with different teams and stakeholders from different business departments.
It is of course not possible to dive into each technical topic at great length; for example, maybe the data quality related sections could be more detailed, but this would make the book much longer. Luckily there are other recent books, such as Automating Data Quality Monitoring: Scaling Beyond Rules with Machine Learning if you want to learn more.
Conclusion: I can easily recommend this book to experienced data engineers, data architects, and business intelligence architects. Even if you might not agree with all of the principles and design choices, I can guarantee that you’ll find yourself thinking and discussing about them in one way or another within your team and various stakeholders in your company, and having a solid resource such as this book will help you to inform and guide such discussions.
Displaying 1 of 1 review