Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS

Amazing content , covered all most everything in DE space. One minor feedback, can also add more content related to Data Governance

+ I like the introductory parts, explains the concept really well.+ Part 2.5 (Architecting DE Pipelines) is one of my favorite chapters.- The book tries to mention a lot of things about data engineering, but it could have focused more on some topics instead of ML and visualization.- I can guess how hard to put hands-on activities in a book but most of the hands-on activities are pretty superficial. For example, everyone knows how to create an S3 bucket or should learn it from a different resource, book, etc.- The only major issue that I've found in this book is the infrastructure-as-code concept. It would be great to mention either Cloudformation or Terraform in this book. No one these days manages its cloud using AWS console.Overall, I found the book pretty informative for people who want to be Data Engineers. It gives all the general idea, however, I found the hands-on activities unsatisfying. This is understandable because the topics are so deep, every chapter can be a separate book. I recommend this book to all Data Engineer candidates.

This is brilliant resource if you want to understand data engineering in a mondern cloud environment

Buy this book if you want to learn some theory and then be shown how to implement the concepts introduced on AWS. This is not a high-brow textbook heavy on theory. It is great for anyone considering using AWS for data warehousing and data engineering, especially generalist infrastructure architects (like myself) who do not focus purely on this space.Gareth deftly sets the scene by providing a useful review of data warehousing technology over time, and how the modern data management landscape has been shaped. Each section of the book starts with a solid theoretical introduction followed by a clear, useful, and most importantly technically detailed how-to guide for implementing each new concept within the AWS environment.I especially like the section on AI / ML; Gareth and his co-contributors succinctly describe how to knit together multiple AWS services to produce a working end-to-end data analytics solution. There are also invaluable bonus sections, such as how to manage a data engineering project, from identifying stakeholders, running whiteboarding sessions, identifying data sources, pipelines, and toolsets.As stated in the introduction, this book alone will not magically turn you into a data engineer. What it will do, however, is give you a solid theoretical and technical foundation in all key concepts, as well as offer insights into future trends. This book is an invaluable point of reference as you progress on your data engineering journey within the AWS ecosystem.

All, Before I share my reviews, I would like to congrats Gareth Eagar, the author of the “Data Engineering with AWS” for design and build cloud-based data transformation pipelines using AWS cloud services. This is one of the great efforts by the author and he has delivered a very detailed and End-to-end Cloud solution in the AWS platform. I could see his trustworthy effort by doing tons of research, sharing his deepest knowledge in cloud services, and demonstrating effectively. He sketches out how the industries are adopting the could technology in the right direction with excess cautions on security and governance point of view with each corner perfectly. Sametime, he didn’t forget to extract the essence of the colourful rainbow from the cloud and presented gorgeous, undoubtedly, and subtly striking bullet points. We must appreciate his deepest knowledge in all these areas of expertise and great mind by coming forward to share with young minds as cloud enthusiastic and those who are already making process in the domain.He has coined the concept of the growing data, and its hidden potential opportunities, challenges with sources, which are required to enhance the business and squeeze the benefits out of it in a right sense by indicting them in total, I mean that how the data has become an important and valuable corporate asset in this current digital scenario and drawing the roadmap for the future perspective.He has clearly drafted and put up the reading route for every reader of this book, that who has the experience in cloud technology with hands-on and alongside he kept in his mind who are all the fresher in a data engineering role, with the best distinguishing boundaries and helping precedes to jump over the chapters to save their time and efforts and guiding succeeded in the queue to undergo the chapters as in the flow towards the rationalized path.Data Cataloging, Security, and Governance chapter is very crucial for every organisation have to build the security dictates and how an organization should protect data to ensure that data is stored securely. The author gave heads-up on core data protection concepts like CCPA, HIPAA and PCI DSS and gave a neat sketch on GDPR as the best sample from the mentioned list above common data regulatory requirements across the globe.Amazon QuickSight, Athena, SageMaker touch basis topics are typical and high demand modules that are very useful for data engineers a knowledge standpoint and who are all closely working withData Scientist and Data Analyst. The author has given an excellent outline on these areas in this book is really welcomed and appreciable work.Hands-on: Wow! One of the amazing stuff in this book is the exclusive “Hand-on” part. I have personally gone through most of them and made my hands are dirty and observed a lot more and enjoyed, such a wonderful “Hands-on” across all the AWS Data Engineering components which are required for implementation, this is really one of the irreplaceable assets for readers, especially the new buddies on the AWS cloud and worthy to do. Here I am insisting to everyone that if you want to be familiar with all required data engineering components, YOU MUST TRY THIS.The author has provided the classical examples by examining of real-world data pipeline from Spotify and Netflix one another needed content in every technical book, I would say.Surely this book will help every data engineer’s needs on understanding the tools available in the cloud for building out complex data analytic projects and understanding which set of tools is best to achieve the outcome needed for their project goals.The overall author has provided a very high quality and streamlined set of instructions throughout in his book right from the beginning in the AWS platform, from step 1 - How to create the AWS account, playing with various components, concepts in and out around it and finally landing on cleaning up your AWS account as well. This is really amazing, and the determination of the author’s special effort is evident.Certainly, I am recommending this book for both categories of people who are all already in the Data Engineering roles and another side that those who are newly started their career in this domain.Overall … I can give 4.5/5 for this. And all the very best to the author.-Shanthababu PandianArtificial Intelligence and Analytics | Cloud Data and ML Architect | Scrum MasterAIML - National and International Speaker | AIML Blogger