Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025
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Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS 2025
Author: Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA ISBN - 978-93-6788-817-9 Preface In today’s digital economy, organizations generate more data in a single day than many legacy systems could process in years. The shift to cloud-first architectures has transformed how we collect, store, and analyze information—enabling businesses to respond faster to market changes, scale without upfront hardware investments, and foster innovation across teams. This book, Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS, is written for data engineers, architects, and technical leaders who seek to design robust, high-performing data platforms using Amazon Web Services. Over the past decade, AWS has introduced a rich portfolio of data services—ranging from serverless ETL (AWS Glue) and streaming solutions (Kinesis, MSK) to petabyte-scale analytics (Redshift, Athena) and machine learning integrations (SageMaker). Yet, with such breadth comes complexity: selecting the right components, designing for cost efficiency, maintaining security and compliance, and ensuring operational excellence are constant challenges. This book distills best practices, architectural patterns, and real-world examples into a cohesive roadmap. You will learn how to build end-to-end pipelines that evolve with your data volume, implement modern data Lakehouse strategies, enable real-time insights, and incorporate governance at every layer. Chapters progress from foundational concepts—such as cloud-first paradigms and core AWS data services—to advanced topics like Data Mesh, serverless Lakehouse’s, generative AI for data quality, and emerging roles in data organization. Each section demystifies the trade-offs, illustrates implementation steps, and highlights pitfalls to avoid. Whether you are migrating legacy workloads, optimizing existing pipelines, or pioneering new analytics capabilities, this book serves as both a practical guide and strategic playbook to navigate the ever-changing landscape of cloud data engineering on AWS. Authors
Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS 2025
Author: Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA ISBN - 978-93-6788-817-9 Preface In today’s digital economy, organizations generate more data in a single day than many legacy systems could process in years. The shift to cloud-first architectures has transformed how we collect, store, and analyze information—enabling businesses to respond faster to market changes, scale without upfront hardware investments, and foster innovation across teams. This book, Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS, is written for data engineers, architects, and technical leaders who seek to design robust, high-performing data platforms using Amazon Web Services. Over the past decade, AWS has introduced a rich portfolio of data services—ranging from serverless ETL (AWS Glue) and streaming solutions (Kinesis, MSK) to petabyte-scale analytics (Redshift, Athena) and machine learning integrations (SageMaker). Yet, with such breadth comes complexity: selecting the right components, designing for cost efficiency, maintaining security and compliance, and ensuring operational excellence are constant challenges. This book distills best practices, architectural patterns, and real-world examples into a cohesive roadmap. You will learn how to build end-to-end pipelines that evolve with your data volume, implement modern data Lakehouse strategies, enable real-time insights, and incorporate governance at every layer. Chapters progress from foundational concepts—such as cloud-first paradigms and core AWS data services—to advanced topics like Data Mesh, serverless Lakehouse’s, generative AI for data quality, and emerging roles in data organization. Each section demystifies the trade-offs, illustrates implementation steps, and highlights pitfalls to avoid. Whether you are migrating legacy workloads, optimizing existing pipelines, or pioneering new analytics capabilities, this book serves as both a practical guide and strategic playbook to navigate the ever-changing landscape of cloud data engineering on AWS. Authors
Practical Data Engineering for Cloud Migration: From Legacy to Scalable Analytics 2025
Author: Author:1- Sanchee Kaushik, Author:1- Prof. Dr. Dyuti Banerjee
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE The exponential growth of data in today’s digital landscape has reshaped how businesses operate, forcing organizations to rethink their data strategies and technologies. As more companies embrace cloud computing, migrating legacy data systems to the cloud has become a critical step towards achieving scalability, flexibility, and agility in data management. “Practical Data Engineering for Cloud Migration: From Legacy to Scalable Analytics” serves as a comprehensive guide for professionals, data engineers, and business leaders navigating the complex but transformative journey of migrating legacy data systems to modern cloud architectures. The cloud has emerged as the cornerstone of modern data infrastructure, offering unparalleled scalability, on-demand resources, and advanced analytics capabilities. However, the transition from legacy systems to cloud-based architectures is often fraught with challenges—ranging from data compatibility issues to migration complexities, security concerns, and the need to ensure that the newly integrated systems perform optimally. This book bridges that gap by providing practical, real-world solutions for overcoming these challenges while focusing on achieving a scalable and high-performing data environment in the cloud. This book is designed to guide readers through every aspect of the cloud migration process. It starts by addressing the core principles of data engineering, data modeling, and the basics of cloud environments. From there, we delve into the specific challenges and best practices for migrating legacy data systems, transitioning databases to the cloud, optimizing data pipelines, and leveraging modern tools and platforms for scalable analytics. The chapters provide step-by-step guidance, strategies for handling large-scale data migrations, and case studies that highlight the successes and lessons learned from real-world cloud migration initiatives. Throughout this book, we emphasize the importance of ensuring that cloud migration is not just a technical task but a strategic business decision. By providing insights into how cloud migration can unlock new opportunities for data-driven innovation, this book aims to empower organizations to make informed decisions, harness the full potential of their data, and move towards more efficient and scalable cloud-native analytics solutions. Whether you are an experienced data engineer tasked with migrating legacy systems or a business leader looking to understand the strategic value of cloud data architectures, this book will provide you with the knowledge and tools necessary to execute a successful cloud migration and set your organization up for future growth. Authors