Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines 1st Edition

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.

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.

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I feel some issues described are already solvable with existing technology making me feel life some things are a bit out of date.

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

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!

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.