Although DataOps expertise is beginning to proliferate, most readers will be somewhere between curious about it to being on a path of implementation. As a data warehousing / BI pro of 15 years, and now beginning the DataOps journey, I find this book to be outstanding. If you are experienced with DW / BI, meaning you have scars, and therefore "wonder" why those initiatives -- which often begin delivering plenty of value -- often got bogged down over the years with technical debt and struggled to demonstrate value and justify budget increases, then you will find this book intriguing. If my word "wonder" does not really capture your emotion about the tech debt and eventual slowing of delivered value, then you will find this book to be like a nearby lightning strike. Despite the scale and ridiculous complexity of the tech stacks at Amazon, Facebook, and other leaders, which lead some to speculate that it must take them so much effort to deploy solutions, their development teamsoften perform multiple releases every day; if your team does not, then you may want to learn what to do about it.At the beginning of this path, two things stand out. First, a whole raft of highly innovative, cloud-first data tools now exist to help us work our way through DataOps towards a more rapid deployment of high quality data sets for analytics. Relying instead on traditional enterprise platforms and tools, for example, for data repositories, ETL, data catalogs, data lineage, data quality, documentation, reporting, etc., will probably hamper the acceleration of time-to-value that you seek, because they tend to lack the newer stuff's features. Secondly, and far more importantly, DataOps calls for the same fundamental organization shifts in team structure, roles and responsiblities, data governance, InfoSec, and the relationship between development and operations as does DevOps, and more. As such, while DevOps is about cultural change, DataOps is even more so and involves interaction with those scary people working outside of IT.DataOps includes two challenges not seen in DevOps. First, with DataOps, a data pipeline (including a set of adequately representative data sets) is inherently more challenging to reproduce, but must be reproduced, in Dev / Test environments than, for example, a software application and it's dataset is for DevOps. Secondly, the data itself, in all it's complexity and imperfection, is a challenge, because our goal is not just ingesting the diverse data without breaking a UI, but also preparing it in a common, query-friendly platform, sometimes without the delays associated with highly-curated, labor-intensive facts and dimension tables. Lastly and importantly, if our DataOps initiative involves evangelizing DataOps to distributed business analytics team who will actually be wrangline the last mile of the data pipeline themselves, then DataOps also involves the associated cat-herding people working outside of IT. If this doesn't sound fun, ask yourself whether it is/was fun to make the business wait for actionable insights or to watch as they bypass IT and continue building stuff piecemeal.So, it's all here: Problem Statement, Vision, Data Strategy, Lean, Agile, Building a Culture of Trust, DevOps fundamentals, organizing teams, and incremental adoption. Chapter 9, "DataOps Technology", is an excellent review of platforms and tools, and will itself probably remain current through 2021 in this fast moving tech sector.The only downside I encountered with this book is that it has a noticeable amount of typos and some imperfect English expression. Still, these never prevented me from fully understanding the context. Balanced against the breadth, depth, clarity of ideas and, yes, the engaging narrative of this book, those concerns are tiny.Five stars!