Practical Data Engineering For Cloud Migration From Legacy To Scalable Analytics 2025
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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 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
Evolving Legacy Systems Transitioning To Microservices And Cloud Native Architectures
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-09
Evolving Legacy Systems Transitioning To Microservices And Cloud Native Architectures written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-09 with Computers categories.
'Evolving Legacy Systems: Transitioning to Microservices and Cloud-Native Architectures' provides a crucial roadmap for organizations aiming to modernize their IT infrastructure. Written for IT professionals, architects, and business leaders, this book demystifies the complexities of transitioning from rigid, outdated systems to flexible, scalable microservices and robust cloud-native solutions. Delving deep into practical strategies, the book guides readers through each phase of the migration process—from the initial assessment and planning stages, through to the decomposition of monolithic systems and data management challenges. It also covers migration to cloud platforms, rigorous testing strategies, and essential monitoring and maintenance practices in new environments. With a focus on actionable insights and proven methodologies, this text equips readers with the knowledge needed to successfully manage the transformation, ensuring that it aligns with business goals and minimizes risk. The clear, factual tone makes complex concepts accessible, turning the theoretical into the achievable. Enhance your organization’s agility, efficiency, and competitive edge with 'Evolving Legacy Systems: Transitioning to Microservices and Cloud-Native Architectures'—an indispensable resource for navigating the challenges and seizing the opportunities of modern IT architecture.
Business Challenges And Opportunities In The Era Of Industry 5 0
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Author : Simon Grima
language : en
Publisher: Emerald Group Publishing
Release Date : 2025-02-14
Business Challenges And Opportunities In The Era Of Industry 5 0 written by Simon Grima 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 2025-02-14 with Business & Economics categories.
Industry 5.0, also known as the fifth industrial revolution, is characterised by advanced technologies such as artificial intelligence, the Internet of Things (IoT), and robotics in manufacturing and other industries. The use of innovative technologies in Industry 5.0 can bring several benefits to companies and the industrial sector, including increased efficiency and productivity, improved product quality, and the ability to create new products and services. In addition, these technologies help businesses to reduce their environmental impact and operate more sustainably. Business Challenges and Opportunities in the Era of Industry 5.0 discusses the development and current technologies within Industry 5.0 and how these apply to various disciplines and sectors such as education, health, finance, production, automotive and construction. This book delves into various Industry 5.0 technologies and how these can improve production and give businesses a competitive edge to remain relevant in a rapidly changing business landscape.The ESFIRM series collects quantitative and qualitative studies in areas relating to finance insurance and risk management. Subjects of interest may include banking, accounting, auditing, compliance, sustainability, behaviour management, and business economics. In the disruption of political upheaval new technologies, climate change and new regulations, it is more important than ever to understand risk in the financial industry. Providing high quality academic research, this book series provides a platform for authors to explore analyse and discuss current and new financial models and theories and engage with innovative research on an international scale.
Architecting High Scale Metrics With Thanos
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24
Architecting High Scale Metrics With Thanos written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.
"Architecting High-Scale Metrics with Thanos" "Architecting High-Scale Metrics with Thanos" is an authoritative guide to designing, deploying, and scaling modern metrics architectures using Thanos and Prometheus. The book opens with a rigorous exploration of distributed metrics systems, dissecting the evolution from monolithic solutions to cloud-native, highly dynamic environments. Readers will gain deep insight into the unique challenges of time-series data, the interplay between metrics, logs, and traces, and the operational complexities of high cardinality, security, and rapid service discovery. Each foundational concept is carefully unpacked to prepare readers for architecting robust observability solutions in today’s rapidly changing infrastructures. Central to this work is a comprehensive treatment of Thanos itself, including its component architecture, deployment topologies, and the motivations for its adoption in environments demanding high scalability, availability, and cost-efficiency. The book provides clear guidance on Prometheus’s limitations at scale, and systematically demonstrates how Thanos extends Prometheus with global querying, long-term object storage, deduplication, and advanced aggregation. Chapters on deploying and operating Thanos offer best practices for Kubernetes-native environments, zero-downtime migrations, cost optimization, and multi-tenancy—equipping engineering teams with real-world strategies for resilient, future-proof observability. Finally, the text offers advanced chapters on securing and automating large Thanos deployments, integrating with a diverse observability ecosystem, and innovating with emerging trends. Topics such as machine learning for anomaly detection, AI-driven retention policies, edge and IoT architectures, cross-cloud observability, and OpenTelemetry integration ensure the book remains at the forefront of the field. Whether you are an engineer, DevOps practitioner, or architect, "Architecting High-Scale Metrics with Thanos" delivers the rigorous technical depth and proven methodologies essential for mastering observability at enterprise scale.
Kubernetes Event Driven Autoscaling With Keda
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-13
Kubernetes Event Driven Autoscaling With Keda written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-13 with Computers categories.
"Kubernetes Event-driven Autoscaling with KEDA" Unlock the full potential of cloud-native architectures with "Kubernetes Event-driven Autoscaling with KEDA," the definitive guide for engineers, architects, and DevOps professionals navigating the complexities of dynamic, event-driven workload scaling. This comprehensive book begins by dissecting Kubernetes autoscaling fundamentals, exploring the mechanisms and limitations of Kubernetes’ native scaling solutions. Readers are introduced to foundational concepts such as the control plane and metric-driven scaling, before delving into the unique challenges associated with unpredictable, spiky workloads typical in modern distributed systems. The heart of the book offers an in-depth exploration into the internals of KEDA—Kubernetes-based Event Driven Autoscaling—illuminating its architecture, integration patterns, and extensibility. Readers will discover advanced techniques for integrating diverse event sources, securing event-driven pipelines, and deploying KEDA seamlessly across hybrid, edge, and multi-cloud environments. Real-world examples demonstrate how to leverage KEDA’s ScaledObjects, ScaledJobs, and custom scaler plugins to enable resilient, responsive scaling for stateless and stateful workloads alike, all while ensuring operational efficiency and cost optimization at scale. Touching on every facet of the event-driven Kubernetes journey, the book addresses instrumentation, observability, and troubleshooting strategies tailored to highly dynamic environments. With sections on performance engineering, predictive autoscaling, building custom scalers, and contributing to the open-source KEDA project, this guide empowers practitioners to design robust, future-proof autoscaling workflows. Rounded out by best practices, anti-patterns, and migration strategies, "Kubernetes Event-driven Autoscaling with KEDA" stands as an essential resource for delivering scalable, reliable, and cost-effective cloud-native applications.
Data Engineering On The Cloud A Practical Guide 2025
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Author : Raghu Gopa, Dr. Arpita Roy
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Data Engineering On The Cloud A Practical Guide 2025 written by Raghu Gopa, Dr. Arpita Roy 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 digital transformation of businesses and the exponential growth of data have created a fundamental shift in how organizations approach data management, analytics, and decision-making. As cloud technologies continue to evolve, cloud-based data engineering has become central to the success of modern data-driven enterprises. “Data Engineering on the Cloud: A Practical Guide” aims to equip data professionals, engineers, and organizations with the knowledge and practical tools needed to build and manage scalable, secure, and efficient data engineering pipelines in cloud environments. This book is designed to bridge the gap between the theoretical foundations of data engineering and the practical realities of working with cloud-based data platforms. Cloud computing has revolutionized data storage, processing, and analytics by offering unparalleled scalability, flexibility, and cost efficiency. However, with these opportunities come new challenges, including selecting the right tools, architectures, and strategies to ensure seamless data integration, transformation, and delivery. As businesses increasingly migrate their data to the cloud, it is essential for data engineers to understand how to leverage the capabilities of the cloud to build robust data pipelines that can handle large, complex datasets in real-time. Throughout this guide, we will explore the various facets of cloud-based data engineering, from understanding cloud storage and computing services to implementing data integration techniques, managing data quality, and optimizing performance. Whether you are building data pipelines from scratch, migrating on-premises systems to the cloud, or enhancing existing data workflows, this book will provide actionable insights and step-by-step guidance on best practices, tools, and frameworks commonly used in cloud data engineering. Key topics covered in this book include: · The fundamentals of cloud architecture and the role of cloud providers (such as AWS, Google Cloud, and Microsoft Azure) in data engineering workflows. · Designing scalable and efficient data pipelines using cloud-based tools and services. · Integrating diverse data sources, including structured, semi-structured, and unstructured data, for seamless processing and analysis. · Data transformation techniques, including ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), in cloud environments. · Ensuring data quality, governance, and security when working with cloud data platforms. · Optimizing performance for data storage, processing, and analytics to handle growing data volumes and complexity. This book is aimed at professionals who are already familiar with data engineering concepts and are looking to apply those concepts within cloud environments. It is also suitable for organizations that are in the process of migrating to cloud-based data platforms and wish to understand the nuances and best practices for cloud data engineering. In addition to theoretical knowledge, this guide emphasizes hands-on approaches, providing practical examples, code snippets, and real-world case studies to demonstrate the effective implementation of cloud-based data engineering solutions. We will explore how to utilize cloud-native services to streamline workflows, improve automation, and reduce manual interventions in data pipelines. Throughout the book, you will gain insights into the evolving tools and technologies that make data engineering more agile, reliable, and efficient. The role of data engineering is growing ever more important in enabling businesses to unlock the value of their data. By the end of this book, you will have a comprehensive understanding of how to leverage cloud technologies to build high-performance, scalable data engineering solutions that are aligned with the needs of modern data-driven organizations. We hope this guide helps you to navigate the complexities of cloud data engineering and helps you unlock new possibilities for your data initiatives. Welcome to “Data Engineering on the Cloud: A Practical Guide.” Let’s embark on this journey to harness the full potential of cloud technologies in the world of data engineering. Authors
Data Engineering With Apache Spark Delta Lake And Lakehouse
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Author : Manoj Kukreja
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-22
Data Engineering With Apache Spark Delta Lake And Lakehouse written by Manoj Kukreja and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Computers categories.
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Data Engineering With Google Cloud Platform
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-31
Data Engineering With Google Cloud Platform written by Adi Wijaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Computers categories.
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Data Engineering With Google Cloud Platform
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-30
Data Engineering With Google Cloud Platform written by Adi Wijaya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-30 with Computers categories.
Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions Key Features Get up to speed with data governance on Google Cloud Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.What you will learn Load data into BigQuery and materialize its output Focus on data pipeline orchestration using Cloud Composer Formulate Airflow jobs to orchestrate and automate a data warehouse Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster Harness Pub/Sub for messaging and ingestion for event-driven systems Apply Dataflow to conduct ETL on streaming data Implement data governance services on Google Cloud Who this book is for Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you.