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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

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Author : Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025 written by Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

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

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Author : Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Practical Data Engineering For Cloud Migration From Legacy To Scalable Analytics 2025

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Author : Author:1- Sanchee Kaushik, Author:1- Prof. Dr. Dyuti Banerjee
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Practical Data Engineering For Cloud Migration From Legacy To Scalable Analytics 2025 written by Author:1- Sanchee Kaushik, Author:1- Prof. Dr. Dyuti Banerjee and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

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

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025

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Author : Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025 written by Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The financial services industry has undergone a profound transformation over the past decade. From high-frequency trading firms demanding millisecond-level insights to retail banks seeking richer, personalized customer analytics, the scale, velocity, and variety of financial data have exploded. Traditional on-premises data warehouses and batch-oriented ETL pipelines struggle to keep pace with today’s requirements for real-time risk monitoring, fraud detection, algorithmic trading signals, and regulatory reporting. In parallel, the rise of cloud computing has unlocked virtually unlimited storage and compute capacity, democratized access to sophisticated analytics tools, and fostered an ecosystem of serverless and managed services designed for elasticity and resilience. This book, Cloud-Native Financial Data Engineering: Principles, Pipelines, and Scalable Architectures, is born out of the need to bridge these trends. It is written for data engineers, architects, and technology leaders who are tasked with designing and operating the next generation of financial data platforms. Whether you are building a streaming pipeline to ingest market quotes, an event-driven system to detect anomalous trading patterns, or a unified data lake that brings together transaction, customer, and risk data, the cloud offers a paradigm shift: you can focus on business logic and analytical value, rather than on undifferentiated heavy lifting of infrastructure. In the chapters that follow, we first establish the foundational principles of cloud-native data engineering in a financial context. We examine how to decompose monolithic ETL workflows into micro-services and pipelines, how to embrace immutable, append-only event stores, and how to design for failure and recovery at every layer. We then explore the core building blocks of modern data architecture: data ingestion patterns (batch, stream, change-data capture), transformation frameworks (serverless functions, containerized jobs, SQL-on-data-lake), metadata management, and orchestration engines. Along the way, we emphasize best practices for security, governance, and cost optimization—imperatives in a regulated, risk-averse industry. Subsequent sections dive into specialized topics that address the unique demands of financial workloads. We cover real-time analytics use cases such as market data enrichment, fraud-signal propagation, and credit-scoring model deployment. We unpack architectural patterns for high-throughput, low-latency pipelines—leveraging managed streaming platforms, serverless compute, column-arithmetic engines, and cloud-native message buses. We also address data quality and lineage at scale, showing how to embed continuous validation tests and visibility into every pipeline stage, thereby ensuring that trading strategies and risk models rest on a bedrock of trusted data. A recurring theme throughout this book is scalability: both horizontal scalability of compute and storage, and organizational scalability via self-service data platforms. We explore how to enable “data as a product” within your enterprise—providing domain teams with curated, discoverable datasets, APIs, and developer tooling so they can build analytics and machine-learning solutions without reinventing ingestion pipelines or wrestling with infrastructure details. This shift not only accelerates time to insight but also frees centralized engineering teams to focus on platform reliability, cost governance, and feature innovation. By combining conceptual frameworks with concrete, provider-agnostic examples, this book aims to be both a roadmap and a practical guide. Wherever possible, we illustrate patterns with code snippets and architectural diagrams, while also pointing to managed services offered by leading cloud providers. We encourage you to adapt these patterns to your organization’s existing standards and to rigorously validate them within your security and compliance constraints. As the lines between “finance” and “technology” continue to blur, the ability to engineer data pipelines that are resilient, elastic, and observably sound becomes a strategic differentiator. Whether you are modernizing a legacy data warehouse, building a next-gen risk platform, or architecting a real-time trading analytics engine, the cloud-native principles and patterns in this volume will equip you to deliver robust, cost-effective solutions that meet the exact demands of financial markets and regulatory bodies alike. We extend our gratitude to the practitioners, open-source contributors, and early adopters whose insights and feedback have shaped this book. It is our hope that by sharing these learnings, we collectively raise the bar for financial data engineering and help usher in an era where data-driven decisions can be made with confidence, speed, and scale. Authors

Ai Personalization Equity And The Future Of Learning

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Author : Wang, Viktor
language : en
Publisher: IGI Global
Release Date : 2025-07-10

Ai Personalization Equity And The Future Of Learning written by Wang, Viktor and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Education categories.

The integration of AI into education has redefined how learning is delivered and measured. There is great potential for AI to drive a more personalized learning experience while also tailoring instructions to a person’s individuals needs. While there are promises to enhance engagement and achievement, it also raises critical questions about equity and access. As we envision the future of learning, it is essential to explore how AI can be harnessed not only to support personalization but also to bridge educational gaps, ensuring that innovation benefits all learners regardless of background or circumstance. AI, Personalization, Equity, and the Future of Learning explores the transformation of AI in education and its impacts on personalized and equitable learning. This book provides a critical lens on equity and access, encouraging the development of inclusive AI-driven solutions that benefit diverse learners worldwide. Covering topics such as academic research, speech recognition tools, and workforce readiness, this book is an excellent resource for researchers, educators, administrators, policymakers, instructional designers, academicians, and more.

Aws Glue For Data Engineers

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-02-02

Aws Glue For Data Engineers written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.

"AWS Glue for Data Engineers: Serverless ETL Made Easy" is an indispensable resource for data engineers seeking to master the art of efficient data integration and transformation in the cloud. This comprehensive guide provides an in-depth exploration of AWS Glue, a powerful tool that streamlines the extract, transform, and load (ETL) processes. Whether you are a novice or an experienced professional, this book is structured to enhance your understanding, covering everything from setup and configuration to advanced features and integrations with other AWS services. Within its pages, readers will discover seamless ways to optimize workflows, harness the full potential of serverless computing, and ensure robust data security and compliance. The book artfully combines practical insights with best practices, guiding you through the complexities of ETL with clear, step-by-step instructions. With real-world use cases and practical examples, it provides a robust framework for leveraging AWS Glue’s capabilities to drive your data engineering tasks, offering solutions to common challenges faced in modern data ecosystems. "AWS Glue for Data Engineers" is not just a technical manual; it’s a strategic roadmap for data professionals striving to enhance their skills in the rapidly evolving field of cloud computing. By adopting its methodologies, you can optimize your ETL workflows, reduce costs, and increase efficiency. Equip yourself with the knowledge to transform your data management practices and create scalable, dynamic systems that meet today’s business demands. Let this book be your guide to unlocking new efficiencies and innovations in your data engineering journey.

Designing Secure And Scalable Iot Systems

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-03

Designing Secure And Scalable Iot Systems written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Computers categories.

"Designing Secure and Scalable IoT Systems" "Designing Secure and Scalable IoT Systems" is a comprehensive guidebook for architects, engineers, and technology leaders seeking to build robust Internet of Things (IoT) applications that meet demanding standards of security, performance, and manageability. Spanning foundational principles through advanced topics, this book examines contemporary IoT architectures, modular system decomposition, and coordination across edge, fog, and cloud environments. It explores event-driven and resilient design patterns, emphasizing scalable data and control plane separation to ensure reliability in rapidly evolving, distributed ecosystems. Central to this work is an in-depth treatment of IoT security fundamentals, including threat modeling, device authentication, lightweight cryptography, secure provisioning, and end-to-end communication protocols. The book rigorously addresses privacy-by-design methodologies, intrusion detection, secure firmware management, and regulatory compliance, equipping readers to anticipate and defend against modern threats while balancing innovation and trust. Advanced security challenges are explored, with insights into zero trust models, quantum-resistant cryptography, anomaly detection, and privacy engineering for compliance with global standards such as GDPR. Beyond security, the book covers every facet of scalable IoT operations — from network design and data management to device lifecycle orchestration, cloud-native deployment, and operational excellence. Readers will find practical strategies for self-healing networks, federated learning, blockchain integration, and sustainable operation at scale. Concluding with emerging trends like decentralized AI, next-generation connectivity, and ethical impact analysis, "Designing Secure and Scalable IoT Systems" serves as an indispensable reference for navigating the complexities and opportunities of next-generation IoT deployments.

Data Engineering For Data Driven Marketing

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Author : Balamurugan Baluswamy
language : en
Publisher: Emerald Group Publishing
Release Date : 2025-03-10

Data Engineering For Data Driven Marketing written by Balamurugan Baluswamy and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-10 with Business & Economics categories.

Offering a thorough exploration of the symbiotic relationship between data engineering and modern marketing strategies, Data Engineering for Data-Driven Marketing uses a strategic lens to delve into methodologies of collecting, transforming, and storing diverse data sources.

Comprehensive Guide To Matillion For Data Integration

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-12

Comprehensive Guide To Matillion For Data Integration written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Computers categories.

"Comprehensive Guide to Matillion for Data Integration" Unlock the full potential of modern cloud data integration with the "Comprehensive Guide to Matillion for Data Integration." This meticulously structured resource provides a deep exploration of contemporary ETL and ELT architectures, equipping readers with the context and clarity needed to navigate an evolving data ecosystem. Through comparative analysis and best-practice recommendations, it situates Matillion within the broader landscape of cloud-native data platforms, addressing the imperatives of scalability, security, and compliance that define today’s enterprise data strategies. From foundational concepts to advanced engineering techniques, the guide walks through every critical stage of deploying, managing, and optimizing Matillion environments. Readers will find practical guidance on architecture fundamentals, project setup, version control, and automated deployments, all crucial for ensuring robust, scalable, and reliable data pipelines. Detailed chapters cover integration with leading cloud data warehouses, operationalization, error handling, and monitoring, empowering data teams to deliver high-quality, resilient workflows under demanding production conditions. Distinguished by its focus on real-world application and future-proofing, the book delves into advanced data engineering practices, governance, security models, and cost optimization. A wealth of patterns and case studies illuminate best practices for both migration and greenfield build-outs, while insights into Matillion’s roadmap prepare readers for the ongoing evolution of cloud-based ETL. Whether you are a data engineer, architect, or platform owner, this guide is an essential companion for leveraging Matillion at enterprise scale.

Aws Greengrass For Edge Computing Solutions

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-09

Aws Greengrass For Edge Computing Solutions written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-09 with Computers categories.

"AWS Greengrass for Edge Computing Solutions" "AWS Greengrass for Edge Computing Solutions" is a definitive guide for architects, developers, and technology leaders aiming to harness the power of edge computing in today's distributed environments. The book opens with a solid grounding in the principles and architectural landscape of edge computing, exploring the evolution of distributed systems, technical drivers for edge deployment, fundamental design patterns, operational challenges, and the strategic differences between edge, fog, and cloud models. Real-world industry use cases illuminate how edge computing is transforming sectors from manufacturing to healthcare, underlining its business relevance and impact. Delving into the heart of AWS IoT Greengrass, the book offers comprehensive coverage of the platform’s internal architecture, secure device identity mechanisms, resource management, and local-to-cloud message routing. Readers are guided through techniques for mass provisioning, automated deployments, robust upgrade strategies, and disaster recovery—all critical for scaling and maintaining resilient edge fleets. The text offers advanced instruction on developing edge applications using Lambda and containers, with a strong emphasis on operational automation, dependency management, secure local resource access, and efficient testing and simulation methodologies. Security and compliance are given detailed attention, with practical guidance on certificate-based authentication, data encryption, zero trust principles, regulatory frameworks such as GDPR and HIPAA, as well as techniques for ongoing security monitoring and response. The book also addresses cutting-edge topics such as edge data analytics, machine learning inference, interoperability in heterogeneous environments, and holistic fleet management. Looking forward, it explores the future of edge innovation, including AI at the edge, 5G connectivity, zero-touch fleets, open-source trends, and the sustainability impact of global edge deployments. Whether deploying at scale or building for tomorrow’s intelligent edge, this resource equips readers to architect, secure, and optimize world-class edge solutions with AWS Greengrass.

Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025

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