The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight 1st Edition

I picked up Sandeep's book while preparing for a data engineering position at a FAANG company. This book was instrumental in my getting a FAANG offer. It helped me structure my answers and really showcase the value I could unleash for the organization. Sandeep's book is steeped in the reality of the steps that are needed to build a high functioning and high-quality data engineering organization and is a must-read for any data/analytics leader. Data engineering is foundational to analytics and data science work. Getting the data processes right will save many organizations a lot of budget, time, and effort. Invest in this book and build your data foundation right from get-go.

A must-read book! As a data engineer, we were struggling to build a data platform that truly satisfies the business needs of the data users(analysts, data scientists, marketers). Following the scorecard approach with 18 measurable metrics, the book helped us identify the services we need to prioritise, and then provided design patterns for different levels of automation for the shortlisted data platform service! Instead of "boiling the ocean" and getting frustrated with no progress for several quarters, this book has been a game changer for my data team. Most importantly, it helped close the gap between data engineering and data scientists using the journey map in the book as a common language.

This book is actually very funny, because it mostly consists of chapter by chapter talking about all the challenges of scaling for data pipelines. It summarizes all the worried thoughts that go through a data manager or data engineers' head as they try to sleep each night. It's useful to summarize the challenges for all parts of the pipeline, because most professionals will not be familiar with all of the challenges or use cases. It's also a good introduction for someone new to this field. However, the value of this book will serve as an historical piece, codifying a time when we were all headscratching how to invent our way out of data platform and efficiency challenges. Many questions; few answers.

There are many useful links inside to go further in your preparation for a career in data. All the chapters are organized in a simple way to cover different topics.