The Data Ops Cookbook: Methodologies and Tools That Reduce Analytics Cycle Time While Improving Quality

by Christopher Bergh, Gil Benghiat, Eran Strod

Understand DataOps in the context of a century-long evolution of ideas that improve how people manage complex systems. It started with pioneers like W. Edwards Deming and statistical process control - gradually these ideas crossed into the technology space in the form of Agile, DevOps and now, DataOps. Preface to the Second Edition Welcome to the Second Edition of the “DataOps Cookbook.” With over 5,000 copies distributed, the first edition of the book far exceeded our expectations. Managers have asked us for boxes of books to distribute to their entire organization. Data professionals are forming study groups around the “DataOps Cookbook.” DataOps is a methodology truly coming of age. The name “DataOps” has always been somewhat problematic, misleading people to believe that we are simply talking about DevOps for data. This misconception started to gain traction in the technical press in mid-2018, shortly after Gartner placed DataOps on the fastest rising part of their Hype Cycle curve for Data Management. In response, we wrote the post “DataOps is NOT Just DevOps for Data” (see the chapter “What is DataOps” below). On Medium, the post has received over 44,000 views (and counting), making it one of the most widely read and referenced thought pieces on data analytics. The DataOps view that analytics is a combination of software development and manufacturing operations seems to have struck a chord within the data industry. The remarkable interest in DataOps has opened the door to many conversations with data professionals, both in individual contributor and management roles. These discussions spurred further thinking about DataOps, and we are now pleased to expand upon the original book with several new additions. We hope these further advance the industry-wide dialogue about data organization productivity and quality. In this latest edition of the DataOps Cookbook, you’ll find the following new · “Warring Tribes into Winning Improving Teamwork in Your Data Organization” on inter-team teamwork · “Improving Teamwork in Data Analytics with DataOps” on intra-team collaboration · “Eliminate Your Analytics Development Bottlenecks” · “A Great Model is Not Enough. Deploying AI Without Technical Debt.” · “The ‘Right To Repair’ DataOps Data Architecture” · “Enabling Design Thinking in Data Analytics with DataOps” Also, we present the surprising results of our DataOps · “Tomorrow’s Cloudy with a Chance of Data Errors - Key Findings of the 2019 DataOps Survey “ DataOps is a foundational topic that requires data teams to fundamentally rethink the ways that they perform their duties. Despite the inherent challenges, we are confident you will find this to be a fruitful and worthwhile endeavor. We look forward to continuing the conversation. -Chris, Gil and Eran This book is for data professionals who are living the nightmare of slow, buggy analytics and frustrated users. It will explain why working weekends isn’t the answer. It provides you with practical steps that you can take tomorrow to decrease your analytics cycle time and virtually eliminate data and analytics errors. And the famous-ish recipe 'DataOps RibEye' included!

258 pages, Kindle Edition

Published January 30, 2020

Reviews

[Adam · Rating 2 out of 5]

Best part for me wasn't particular to DataOps, but is more for getting buy-in from stakeholders, paraphrased as:

1. Interview stakeholders on their problems and goals. Steer any discussions of solutions into outcomes (instead of "let's build this", shoot for "let's get to this place"). From the book: "A good way to phrase desired outcomes is in terms of the type (minimize, increase) and quantity (time, number, frequency) of improvement required."
2. List the desired outcomes out (maybe annotate with who said what
3. Survey stakeholders to rate the importance of each and how well it's satisfied today.
4. Prioritize based on 2 * importance - satisfaction (add importance to gap)

Beyond that, I felt it was a bit too repetitive with too few details and largely a reinforcement of my existing opinions.