Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro

As an aspiring Data Engineer, navigating through the myriad concepts and technologies is a daunting task. In the realm of modern Data Engineering, the cloud is undeniably a linchpin. It was precisely this emphasis on cloud-centric knowledge that drew me to Gareth Eagar's book, and having perused the reviews of the first edition, my conviction in its high-quality content was cemented.I found my expectations to be well-founded; the book seamlessly blends high-quality conceptual information with hands-on activities. It serves as an invaluable resource, offering a comprehensive guide to understanding the intricacies of data engineering in the era of cloud computing. The content not only equips me with the requisite knowledge but also provides practical insights through hands-on exercises.This book is more than just a learning tool; it's a roadmap that propels aspiring Data Engineers into a competitive stance within the dynamic Data Engineering market. Gareth Eagar's meticulous approach not only lays the groundwork for my future career but also positions me for success in obtaining the coveted AWS Data Engineer certification.I extend my gratitude to Gareth Eagar for crafting such an impactful resource. The dedication poured into this book is palpable, evident in the seamless flow of content and hands-on activities. The clarity with which concepts are elucidated has bridged the gaps in my understanding, making this book an indispensable companion in my journey to mastering Data Engineering with AWS."

I enjoyed every bit of this book from the beginning to the end. I think the subsequent edition should be focused on how to setup robust pipelines in a production environment. Kudos to the author.

This is a great book for readers to study Data engineering using AWS from basic concepts to advanced topics. It covered broadly and is suitable to readers in different levels.

First time I've even written a review for a book on Amazon. That's how good this book is. Gives you enough theory with practical examples, use cases and hands-on experience in AWS.

"Data Engineering with AWS" by Gareth Eagar and published by Packt is an indispensable guide for aspiring data engineers navigating the complex landscape of big data analytics on Amazon Web Services. The book's comprehensive structure, divided into three sections, begins with foundational AWS Data Engineering Concepts and Trends. The initial chapters cover key topics such as the challenges of burgeoning datasets, cloud benefits, and hands-on AWS account setup. Section two delves into Architecting and Implementing Data Lakes and Data Lake Houses, providing practical insights into pipeline architecture, data ingestion, transformation, and loading into data marts. The final section explores the broader context of Data Analytics, Visualization, and Machine Learning, offering hands-on experiences with Amazon Athena, QuickSight, and AWS services for AI and ML.Eagar's hands-on approach is a standout feature, ensuring readers gain practical experience in each aspect discussed. The book excels in demystifying complex concepts, from orchestrating data pipelines to optimizing analytics with Amazon Athena. With its clear explanations, real-world examples, and forward-looking perspective on emerging trends, this book is an invaluable resource for anyone looking to master data engineering on AWS.

Explore the extensive landscape of AWS data engineering with "Data Engineering with AWS." Tailored for cloud engineers, DevOps professionals, developers, architects, and IT experts, this book covers the entire spectrum of data transformation pipelines.Starting with an introduction to data engineering challenges and the pivotal role of a data engineer, the book progresses to foundational concepts and technologies for big data processing. It guides readers through a wide range of AWS services for data ingestion, processing, and orchestration.Crucial topics such as data governance, security, and cataloging are addressed, providing a holistic understanding of building resilient and secure cloud infrastructures.The book goes beyond basics, offering insights into architecting data engineering pipelines, handling batch and streaming data, and optimizing datasets for analytics. It also explores advanced concepts like data marts, Amazon Redshift, and orchestrating data pipelines.With a focus on practicality, the book covers ad hoc queries with Amazon Athena, data visualization with Amazon QuickSight, and the integration of artificial intelligence and machine learning into data engineering workflows.It concludes by discussing emerging trends in the industry, providing real-world examples of data pipelines, and offering a comprehensive overview of data analytics.

This is a nice practical book. One of the book's strengths is its practical approach. The author not only explains theoretical concepts but also provides real-world examples that make the learning process engaging and relevant. Each chapter is embedded with exercises that encourage readers to apply what they've learned.