Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines

As lead data engineer in a teenage software company looking to be data-driven, this book showed me the path and the current estate on data pains at scale. We're currently facing some of those. During the reading of the book, I could set the right choises on when invest on data quality frameworks. This book summarizes preetty good the actual state of data management and the key points on what you should take care if your company wants the next level of data assests. It is a should read if you're a executive or a leader who promotes data investments. This book will clear the path and the ammounth of effort that organizations needs to deal with in terms of survive and innovate. There was an item that I personally would change, that was the connection with data mesh. I trully love the all content published by Monte Carlo data, specially Barr, she is quite a data rockstar this days. I also love the book and contect on data mesh, but i felt the connection pretty forced. I'd love to see in a near future another book like this one. A book written by THE EXPERT on data quality, management, and of course observability.

Excellent content about data quality insights, robust conceptually about Service level agreements and design strategies to ensuring the best quality. However the print quality is not the best, the plot does not have color

Many books and tutorials have been written about “data quality” and basically what that means. However, this book takes singular aim on projects such as data pipelines, data warehousing, data integrations, business intelligence/analytics, data lakes, big data, and other types of data ETLs. It was all smartly done by the authors. The book reiterates that “many data engineering teams face “good pipelines, bad data problems; and good data pipeline infrastructures, but often with bad data”. Although I’ve searched long and hard to find a book like this to guide me in data pipeline quality and testing, this is the most comprehensive.

This book is new on my shelf, and it already serves as my foremost tool for handling any issues with data. The book is smart, comprehensive yet concise, and extremely practical. I liked the direct approach and real world examples that lead you to understand the basic problems and offer ways to handle them. A gem of a book!

The fundamentals covered in this book are excellent points and the interviews done really add real life feedback to power the concepts. That being said, it was rather repetitive at many points along the book and I feel some issues described are already solvable with existing technology making me feel life some things are a bit out of date.

As a beginner in the field, I've done my own independent research regarding data quality, but what I like the most about this particular text is the comprehensiveness and also insight from the case studies. Good read.

There are almost-identical sections throughout the whole book, it is poorly edited and structure. Highly repetitive. Save your money

The book feels like a marketing book to advertise the author's own business company. The content is so shallow that it really doesn't bring any value if you're already practicing data engineering for, let's say, one or two years. While the content seems to be good on the surface, it really doesn't take time to lay the foundations and highlight theoretical aspects enough. It's more like "a mile wide and an inch deep". Almost every important aspect only gets a very small paragraph in the book without proper guidance or explanation how to do it in practice. Here an example: There's a section called "Scaling Anomaly Detection with Python and Machine Learning". So far, so good. However, except for the mentioning of some Python libraries and the usage of sqlite3 there is no Python involved. And the ML techniques mentioned are simply only measuring techniques of model performance. No, "real" ML involved and only mentioned as a side note that you could use (e.g.) regression techniques to improve. Sorry, but this isn't really helpful in the end. The book is full of this kind of examples. Mentioning something without really putting it into practice but then delivering really detailed SQL examples for things that are really simple and wasting time with useless case studies. To me, it feels like the author's only wrote this shallow book to highlight their own business. I always take notes when I read books. I collect the things that I think are good to know for my day-to-day job. Let's say I haven't read a book in a long time where I wrote down so few things that I think are relevant for my job. For me, this reflects the actual density of the content of this book.