Aws Data Engineering For Modern Analytics
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Aws Data Engineering For Modern Analytics
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Author : Frank Reiniger
language : en
Publisher: Independently Published
Release Date : 2025-11-05
Aws Data Engineering For Modern Analytics written by Frank Reiniger and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-05 with Computers categories.
AWS Data Engineering for Modern Analytics What if your data pipelines didn't break at scale, no surprise bills, no late-night firefights, no silent failures? In a world where cloud-native analytics defines competitive advantage, simply collecting data isn't enough. Enterprises need platforms that are secure, auditable, cost-efficient, and engineered to survive real-world complexity. This book is your practical blueprint for building production-ready data systems on AWS. It strips away hype and focuses on the reality facing modern data teams: how to architect lakes on S3 with intent, how to run Glue and EMR without waste, how to orchestrate with Step Functions and CI/CD instead of ad-hoc scripts, and how to design pipelines that evolve safely as your business grows. At its heart, this guide solves the biggest challenge in cloud data engineering-moving from prototypes that "work" to platforms you can trust with mission-critical workloads. You will learn how to: Structure S3 data lakes with the right formats, partitions, and lifecycle rules Build incremental ETL pipelines with Glue that handle schema changes and retries Implement real-time streaming with Kinesis and Flink for event-driven analytics Design secure, governed environments with IAM, Lake Formation, and encryption Deliver ML-ready feature pipelines and integrate with SageMaker Observe pipeline health, enforce SLAs, and prevent silent data drift Deploy reliable infrastructure using Terraform/CloudFormation and automated CICD Through hands-on labs and real deployment patterns, you'll master the engineering fundamentals behind cost control, operational resilience, metadata design, multi-environment workflows, disaster recovery, and future-proof storage formats like Apache Iceberg. If you're a data engineer, architect, analytics leader, or cloud practitioner committed to building systems that don't crumble under real workloads, this book will elevate your execution and confidence. Build with precision. Ship with certainty. Own your data platform, not the other way around. Get your copy and start engineering AWS pipelines the right way, today.
Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025
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Author : AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025 written by AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK 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 has redefined the way organizations operate, compete, and innovate. In today’s digital era, businesses are no longer just consumers of data but active participants in building complex, scalable ecosystems that collect, process, store, and derive value from massive data streams. Amazon Web Services (AWS), as the world’s leading cloud platform, offers a robust suite of tools and services that empower enterprises to transform raw data into actionable insights with unprecedented speed and reliability. This book, Advanced Data Engineering on AWS: Building Scalable, Secure, and Intelligent Pipelines, is designed to guide readers through the essential foundations and evolving innovations in data engineering using AWS. It systematically covers the principles and practices needed to architect high-performance data pipelines that can handle modern business demands. The journey begins with establishing the Foundations of Data Engineering in the AWS Ecosystem, helping readers understand how AWS services interplay to create a seamless environment for data management. We then explore Designing Data Pipelines for Scalability and Reliability, focusing on the architectural patterns that ensure resilience and flexibility in an unpredictable data landscape. As data sources become increasingly diverse and dynamic, mastering Data Ingestion Techniques on AWS is critical. We delve into both batch and real-time ingestion strategies, enabling efficient collection of high-velocity data. Coupled with this is Data Storage Optimization using services like S3, Redshift, and Beyond, ensuring that storage solutions align with both performance and cost-efficiency goals. Understanding ETL and ELT on AWS is pivotal for preparing data for downstream analytics and machine learning tasks. Subsequently, Real-Time Data Processing on AWS highlights how to transform and analyze data streams to deliver timely, business-critical insights. Automation becomes key as we address Data Orchestration and Workflow Automation, enabling complex pipelines to run with minimal human intervention. Ensuring trust in data requires rigorous focus on Data Quality and Governance, laying a strong foundation for secure, compliant, and high-fidelity analytics. We further extend this security narrative in Security and Compliance in AWS Data Pipelines, offering a deep dive into encryption, access controls, and regulatory alignment. No modern pipeline is complete without observability; hence, Monitoring, Logging, and Performance Tuning explores techniques to gain actionable insights into pipeline behavior, prevent failures, and optimize operations proactively. In an increasingly globalized world, Advanced Architectures: Multi-Region and Hybrid Pipelines prepares readers for designing architectures that span geographic—es and cloud environments, ensuring data availability and fault tolerance. Finally, we look ahead to Future Trends: AI/ML-Driven Data Engineering on AWS, where artificial intelligence automates data engineering tasks, adaptive pipelines become reality, and next-generation solutions redefine how businesses leverage data at scale. This book aims to serve data engineers, architects, cloud practitioners, and technical leaders who seek to not only build scalable AWS-based systems but also future-proof their architectures in an evolving technology landscape. Through a blend of foundational principles, hands-on techniques, best practices, and forward-looking insights, this book is your comprehensive guide to mastering advanced data engineering on AWS. We invite you to embark on this journey to build the data systems that will power the intelligent enterprises of tomorrow. Authors Gayatri Tavva Dr Priyanka Kaushik
Data Engineering With Aws
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Author : Gareth Eagar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-12-29
Data Engineering With Aws written by Gareth Eagar 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 2021-12-29 with Computers categories.
The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.
Aws Certified Data Engineer Associate Study Guide
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Author : Sakti Mishra
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-08-25
Aws Certified Data Engineer Associate Study Guide written by Sakti Mishra and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-25 with Computers categories.
There's no better time to become a data engineer. And acing the AWS Certified Data Engineer Associate (DEA-C01) exam will help you tackle the demands of modern data engineering and secure your place in the technology-driven future. Authors Sakti Mishra, Dylan Qu, and Anusha Challa equip you with the knowledge and sought-after skills necessary to effectively manage data and excel in your career. Whether you're a data engineer, data analyst, or machine learning engineer, you'll discover in-depth guidance, practical exercises, sample questions, and expert advice you need to leverage AWS services effectively and achieve certification. By reading, you'll learn how to: Ingest, transform, and orchestrate data pipelines effectively Select the ideal data store, design efficient data models, and manage data lifecycles Analyze data rigorously and maintain high data quality standards Implement robust authentication, authorization, and data governance protocols Prepare thoroughly for the DEA-C01 exam with targeted strategies and practices
Data Engineering With Generative And Agentic Ai On Aws
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Author : Justin J. Leto
language : en
Publisher: Apress
Release Date : 2026-04-11
Data Engineering With Generative And Agentic Ai On Aws written by Justin J. Leto and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-04-11 with Computers categories.
Unlock the future of cloud data engineering with generative and agentic AI on AWS. This hands-on guide shows you how to build intelligent, responsive data platforms using cutting-edge AI capabilities and modern AWS services. Learn to design next-generation data architectures—from data lakes and data mesh to scalable pipelines and real-time analytics. Discover how generative AI and agentic automation are transforming every aspect of enterprise data work: ingesting unstructured data, enabling semantic search with Retrieval-Augmented Generation (RAG), building autonomous data agents, and using natural language interfaces to turn business questions into instant insights. Author Justin J. Leto, PE, MBA, PMP, is a Principal Solutions Architect at AWS with over 20 years of experience in data engineering and AI. He doesn't just teach today's techniques—he prepares you for the future disruptions reshaping the field. His book is essential reading for current and aspiring data engineers, data analysts, data architects, engineering managers, CTOs, CDOs, and data-focused entrepreneurs looking to gain an edge over the competition. What You Will Learn: Master the core principles and practices of data engineering to build a long, successful career in the field. Accelerate your impact using AWS cloud services for scalable, modern data solutions. Explore how the role of the modern data engineer is evolving to support generative and agentic AI use cases. Develop a modern data strategy by working backwards from business goals to gain buy-in from CxO-level leadership. Design and deploy modern data architectures—including data lakes, data mesh, and data marts—and understand when to use each. Apply generative and agentic AI to enhance every stage of the data engineering lifecycle. Evaluate emerging data and AI technologies using proven methodology to separate real value from hype. Prepare for the future of data engineering powered by autonomous agents that scale enterprise impact. Who this Book Is For: Data engineers, analysts, architects, and tech leaders seeking practical guidance on AWS data engineering and generative AI, with or without prior cloud experience.
97 Things Every Data Engineer Should Know
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Author : Tobias Macey
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-11
97 Things Every Data Engineer Should Know written by Tobias Macey and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Computers categories.
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
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.
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.
Modern Data Architecture On Aws
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Author : Behram Irani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-08-31
Modern Data Architecture On Aws written by Behram Irani 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 2023-08-31 with Computers categories.
Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.
Data Analytics In The Aws Cloud
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Author : Joe Minichino
language : en
Publisher: John Wiley & Sons
Release Date : 2023-04-06
Data Analytics In The Aws Cloud written by Joe Minichino and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-06 with Computers categories.
A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.