What do you think?
Rate this book
Data Management at Scale: Best Practices for Enterprise Architecture
As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learn how to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.
345 pages, Paperback
Published September 1, 2020
68 people are currently reading
454 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 13 of 13 reviews
April 16, 2022
This did not work for me.. i had very different expectations from this book, seeing that’s a new release, I was expecting for it to have more modern takes, but the way it was written, to me it felt that the information offered was not very practical and extremely extremely enterprisey.
Probably better suited for data teams that are more business focused than the engineering Data Platform teams.
Probably better suited for data teams that are more business focused than the engineering Data Platform teams.
September 14, 2020
Well structured, covering many data design aspects. Lots of data integration patterns and a wider data governance view. Must read for all data professionals!
October 19, 2020
Very useful overview of modern data management disciplines, all glued together in an proposed architecture concept. Highly recommended for all data architects!
October 17, 2021
Summary:
It is an excellent book about data architecture in a modern company, but unfortunately, it lacks real-life examples, so 4.5/5
As the title suggests, it is mostly about enterprise solutions, so do not expect that patterns here will be essential for you if you are a smaller company, but still, it's worth reading it to know what you can expect in the future.
Pros.
- Gives an excellent overview of 3 main concepts of data communication patterns: RDS, API & Event Streaming.
- A lot, really, a lot of information about metadata concepts, values & patterns. Thanks to it, I finally understood why this is so important.
- It covers all from the data warehouse to data mesh and adds examples of connecting all of those dots.
- It probably contains the best explanation of what a golden source (source of truth) is and why this is so important in a distributed world.
- A lot of links to different open-source packages or commercial solutions to solve particular issues described in the book.
Cons
- All examples in the book are very high-level. It'd be great if there were some appendix with more real-life problems.
It is an excellent book about data architecture in a modern company, but unfortunately, it lacks real-life examples, so 4.5/5
As the title suggests, it is mostly about enterprise solutions, so do not expect that patterns here will be essential for you if you are a smaller company, but still, it's worth reading it to know what you can expect in the future.
Pros.
- Gives an excellent overview of 3 main concepts of data communication patterns: RDS, API & Event Streaming.
- A lot, really, a lot of information about metadata concepts, values & patterns. Thanks to it, I finally understood why this is so important.
- It covers all from the data warehouse to data mesh and adds examples of connecting all of those dots.
- It probably contains the best explanation of what a golden source (source of truth) is and why this is so important in a distributed world.
- A lot of links to different open-source packages or commercial solutions to solve particular issues described in the book.
Cons
- All examples in the book are very high-level. It'd be great if there were some appendix with more real-life problems.
January 30, 2022
The coming years will see a paradigm shift for organizations that place data at the heart of their business. Data will be much more distributed in the years to come—and so will data ownership. It means we will need new approach to manage, process and using data. For supporting this change we need an architecture that provide necessary tools and capabilities for this change. Author introduces Scaled Architecture.
“What enterprises need is a scalable and highly distributed architecture that can easily connect data providers and data consumers while providing flexibility, control, and insight. This architecture is called a Scaled Architecture.”
Book examined different aspect of scaled architecture and explains best practices, tools and technologies for building such an architecture. Managing, processing and providing huge volume of data with high flexibility is main aspect of this architecture.
If you have the responsibility of providing vast amount data for using with customers, I recommend you to study this book. You can find a lot of inspiration about architecture and data management in this book.
“What enterprises need is a scalable and highly distributed architecture that can easily connect data providers and data consumers while providing flexibility, control, and insight. This architecture is called a Scaled Architecture.”
Book examined different aspect of scaled architecture and explains best practices, tools and technologies for building such an architecture. Managing, processing and providing huge volume of data with high flexibility is main aspect of this architecture.
If you have the responsibility of providing vast amount data for using with customers, I recommend you to study this book. You can find a lot of inspiration about architecture and data management in this book.
June 4, 2023
This is probably one of those books you need to get through more than once. Or that you throw away after chapter 2. Love it or hate it.
A very, very intense book. Get ready for a huge amount of topics to be thrown at you all at once. While there are examples, they usually come after theory. I would do the other way around as it makes it so much easier to understand. The book relies heavily on the seeds of Domain Driven Designs and Design Patterns of the early 2000s. If you do not know those topics, don't give up, brace and prepare for some wtf!? But at some point it all makes sense. Just get back and give it a second or third pass. It is worth it, tho.
4 stars for the overall content. 1 for the poor organization and complexity
A very, very intense book. Get ready for a huge amount of topics to be thrown at you all at once. While there are examples, they usually come after theory. I would do the other way around as it makes it so much easier to understand. The book relies heavily on the seeds of Domain Driven Designs and Design Patterns of the early 2000s. If you do not know those topics, don't give up, brace and prepare for some wtf!? But at some point it all makes sense. Just get back and give it a second or third pass. It is worth it, tho.
4 stars for the overall content. 1 for the poor organization and complexity
February 15, 2021
Great book, describes data architecture in modern world. Very inspiring!
April 28, 2021
Extensive resource on data-management. A MUST read for anyone involved in data governance.
October 28, 2023
As a professional in this field I continuously search and read books to expand and enrich my knowledge. I like that this one is focused on Enterprise Architecture, nearer to my current challenge in a corporation instead of just providing generic concepts on data that many times don’t apply to the real world.
The book it's from 2020 and this edition from 2021. I didn’t find brand new concepts but a refresh on topics that I am well aware of. Probably for someone getting started as a technical manager in this field it will provide more value because it’s well structured and presents topics in an easy-to-follow scheme with excellent definitions of key concepts like data architecture, data governance, data warehousing, etc. What I like most about it is that it’s clear that the author works at a company (ABN AMRO) and has faced many of the challenges that we as data leaders deal with everyday.
The author presents Scale Architecture and the components he uses for this framework like golden source and golden dataset as a sweet spot between a raw and chaotic strategy and the other extreme of being too tight and not flexible enough. As with any of these playbooks, you’ll probably find some elements to use and discard the rest but you know that this best practices come from someone who tested them on the field (unlike other big data self claim gurus that only give talks because they are more speakers than practitioners). He draws concepts from the software design discipline and provides many authors and books to deep dive on what he presents here. After the first chapters the book becomes more technical because it aims at architects. If you are a manager like me maybe you don't need so much detail for your job but at least for me it's useful to interact with the technical stakeholders.
Some interesting fragments:
“In a highly distributed ecosystem, is it really the best way to bring all data centrally before it can be consumed by any user or application?”
“The transformation of a monolithic application into a distributed application created many challenges for data management.”
“Because of the data warehouse’s high degree of complexity and one central team managing it, the lack of agility often becomes a concern.”
“Warehouses are becoming more scattered and are forced to export data.”
“Warehouses often lack insight into ad hoc consumption.”
“Datalake implementation failure rate of more than 60%” (Nick Heudecker from Gartner)
“By removing data professionals from business domains, we take away creativity and business insights.”
The book it's from 2020 and this edition from 2021. I didn’t find brand new concepts but a refresh on topics that I am well aware of. Probably for someone getting started as a technical manager in this field it will provide more value because it’s well structured and presents topics in an easy-to-follow scheme with excellent definitions of key concepts like data architecture, data governance, data warehousing, etc. What I like most about it is that it’s clear that the author works at a company (ABN AMRO) and has faced many of the challenges that we as data leaders deal with everyday.
The author presents Scale Architecture and the components he uses for this framework like golden source and golden dataset as a sweet spot between a raw and chaotic strategy and the other extreme of being too tight and not flexible enough. As with any of these playbooks, you’ll probably find some elements to use and discard the rest but you know that this best practices come from someone who tested them on the field (unlike other big data self claim gurus that only give talks because they are more speakers than practitioners). He draws concepts from the software design discipline and provides many authors and books to deep dive on what he presents here. After the first chapters the book becomes more technical because it aims at architects. If you are a manager like me maybe you don't need so much detail for your job but at least for me it's useful to interact with the technical stakeholders.
Some interesting fragments:
“In a highly distributed ecosystem, is it really the best way to bring all data centrally before it can be consumed by any user or application?”
“The transformation of a monolithic application into a distributed application created many challenges for data management.”
“Because of the data warehouse’s high degree of complexity and one central team managing it, the lack of agility often becomes a concern.”
“Warehouses are becoming more scattered and are forced to export data.”
“Warehouses often lack insight into ad hoc consumption.”
“Datalake implementation failure rate of more than 60%” (Nick Heudecker from Gartner)
“By removing data professionals from business domains, we take away creativity and business insights.”
September 28, 2021
This is one of the best data Architecture books that I have ever read. The book beautifully addresses the disruption of data management in large scales.
It is a dense book that runs the whole gamut of data architecture, from data domains to metadata management.
It is highly recommended for modern enterprise architects that care about the way data flows through company.
It is a dense book that runs the whole gamut of data architecture, from data domains to metadata management.
It is highly recommended for modern enterprise architects that care about the way data flows through company.
July 24, 2021
Great read on modern data management. Could use a bit more practical examples, in my opinion.
January 15, 2023
I stopped reading at the moment when the author called Data warehouses and data lakes “ big ball of mud” without any reasonable arguments.
October 21, 2023
Very useful insights for enterprise data management. If you can get the 2nd edition, it was updated with new data management ideas and has more reference implementation.
Displaying 1 - 13 of 13 reviews