Data Engineering with AWS Cookbook serves as a comprehensive practical guide for building scalable and efficient data engineering solutions using AWS. With this book, you will master implementing data lakes, orchestrating data pipelines, and creating serving layers using AWS's robust services, such as Glue, EMR, Redshift, and Athena. With hands-on exercises and practical recipes, you will enhance your AWS-based data engineering projects. What this Book will help me do Gain the skills to design centralized data lake solutions and manage them securely at scale. Develop expertise in crafting data pipelines with AWS's ETL technologies like Glue and EMR. Learn to implement and automate governance, orchestration, and monitoring for data platforms. Build high-performance data serving layers using AWS analytics tools like Redshift and QuickSight. Effectively plan and execute data migrations to AWS from on-premises infrastructure. Author(s) Trâm Ngọc Phạm, Gonzalo Herreros González, Viquar Khan, and Huda Nofal bring together years of collective experience in data engineering and AWS cloud solutions. Each author's deep knowledge and passion for cloud technology have shaped this book into a valuable resource, geared towards practical learning and real-world application. Their approach ensures readers are not just learning but building tangible, impactful solutions. Who is it for? This book is geared towards data engineers and big data professionals engaged in or transitioning to cloud-based environments, specifically on AWS. Ideal readers are those looking to optimize workflows and master AWS tools to create scalable, efficient solutions. The content assumes a basic familiarity with AWS concepts like IAM roles and a command-line interface, ensuring all examples are accessible yet meaningful for those seeking advancement in AWS data engineering.

Learn data engineering and modern data pipeline design with AWS in this comprehensive guide! You will explore key AWS services like S3, Glue, Redshift, and QuickSight to ingest, transform, and analyze data, and you'll gain hands-on experience creating robust, scalable solutions. What this Book will help me do Understand and implement data ingestion and transformation processes using AWS tools. Optimize data for analytics with advanced AWS-powered workflows. Build end-to-end modern data pipelines leveraging cutting-edge AWS technologies. Design data governance strategies using AWS services for security and compliance. Visualize data and extract insights using Amazon QuickSight and other tools. Author(s) Gareth Eagar is a Senior Data Architect with over 25 years of experience in designing and implementing data solutions across various industries. He combines his deep technical expertise with a passion for teaching, aiming to make complex concepts approachable for learners at all levels. Who is it for? This book is intended for current or aspiring data engineers, data architects, and analysts seeking to leverage AWS for data engineering. It suits beginners with a basic understanding of data concepts who want to gain practical experience as well as intermediate professionals aiming to expand into AWS-based systems.

In "Geospatial Data Analytics on AWS," you will learn how to store, manage, and analyze geospatial data effectively using various AWS services. This book provides insight into building geospatial data lakes, leveraging AWS databases, and applying best practices to derive insights from spatial data in the cloud. What this Book will help me do Design and manage geospatial data lakes on AWS leveraging S3 and other storage solutions. Analyze geospatial data using AWS services such as Athena and Redshift. Utilize machine learning models for geospatial data processing and analytics using SageMaker. Visualize geospatial data through services like Amazon QuickSight and OpenStreetMap integration. Avoid common pitfalls when managing geospatial data in the cloud. Author(s) Scott Bateman, Janahan Gnanachandran, and Jeff DeMuth bring their extensive experience in cloud computing and geospatial analytics to this book. With backgrounds in cloud architecture, data science, and geospatial applications, they aim to make complex topics accessible. Their collaborative approach ensures readers can practically apply concepts to real-world challenges. Who is it for? This book is ideal for GIS and data professionals, including developers, analysts, and scientists. It suits readers with a basic understanding of geographical concepts but no prior AWS experience. If you're aiming to enhance your cloud-based geospatial data management and analytics skills, this is the guide for you.

Discover how to effectively build and manage data engineering pipelines using AWS with "Data Engineering with AWS". In this hands-on book, you'll explore the foundational principles of data engineering, learn to architect data pipelines, and work with essential AWS services to process, transform, and analyze data. What this Book will help me do Understand and implement modern data engineering pipelines with AWS services. Gain proficiency in automating data ingestion and transformation using Amazon tools. Perform efficient data queries and analysis leveraging Amazon Athena and Redshift. Create insightful data visualizations using Amazon QuickSight. Apply machine learning techniques to enhance data engineering processes. Author(s) None Eagar, a Senior Data Architect with over twenty-five years of experience, specializes in modern data architectures and cloud solutions. With a rich background in applying data engineering to real-world problems, None Eagar shares expertise in a clear and approachable way for readers. Who is it for? This book is perfect for data engineers and data architects aiming to grow their expertise in AWS-based solutions. It's also geared towards beginners in data engineering wanting to adopt the best practices. Those with a basic understanding of big data and cloud platforms will find it particularly valuable, but prior AWS experience is not required.