Data Engineering With Generative And Agentic Ai On Aws

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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.

Generative Ai For Software Developers

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Author : Saurabh Shrivastava
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-10-10

Generative Ai For Software Developers written by Saurabh Shrivastava 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 2025-10-10 with Computers categories.

Master Generative AI in software development with hands-on guidance, from coding and debugging to testing and deployment, using GitHub Copilot, Amazon Q Developer, and OpenAI APIs to build scalable, AI-powered applications Key Features Hands-on guidance for mastering AI-powered coding, debugging, and deployment with real-world examples Comprehensive coverage of GenAI concepts, prompt engineering, fine-tuning, and SDLC integration Practical strategies for architecting and scaling production-ready AI-driven applications Book DescriptionGenerative AI for Software Developers is your practical guide to mastering AI-powered development and staying ahead in a fast-changing industry. Through a structured, hands-on approach, this book helps you understand, implement, and optimize Generative AI in modern software engineering. From AI-assisted coding, debugging, and documentation to testing, deployment, and system design, it equips you with the skills to integrate AI seamlessly into your workflows. You’ll work with tools such as GitHub Copilot, Amazon Q Developer, and OpenAI APIs while learning strategies for prompt engineering, fine-tuning, and building scalable AI-powered applications. Featuring real-world use cases, best practices, and expert insights, this book bridges the gap between experimenting with AI and production deployment. Whether you’re an aspiring AI developer, experienced engineer, or solutions architect, this guide gives you the clarity, confidence, and tactical knowledge to thrive in the GenAI-driven future of software development. Armed with these insights, you’ll be ready to build, integrate, and scale intelligent solutions that enhance every stage of the software development lifecycle.What you will learn Build a secure GenAI application with expert guidance Understand the fundamentals of GenAI and its applications in software engineering Automate coding tasks with tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIs Apply AI for debugging, testing, documentation, and deployment workflows Get to grips with prompt engineering and fine-tuning techniques to optimize AI outputs Implement best practices for architecting and scaling AI-powered applications Build end-to-end GenAI projects, moving from experimentation to production Who this book is for This book is for software developers, engineers, architects, and tech professionals who want to understand the core concepts of Generative AI and its real-world applications, master AI-driven development workflows to improve efficiency and code quality, and leverage tools like GitHub Copilot, Amazon Q Developer, and OpenAI APIs to automate coding tasks.

Generative Ai On Aws Building Multimodal Generative Ai Applications

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Author : Chris Fregly
language : en
Publisher: O'Reilly Media
Release Date : 2024-01-30

Generative Ai On Aws Building Multimodal Generative Ai Applications written by Chris Fregly and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-30 with Computers categories.

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. In this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, machine learning practitioners, business analysts, data engineers, and data scientists find a practical way to use this exciting new technology. You'll learn the generative AI project lifecycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), model quantization, optimization, and deployment. You'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and video. You'll also be able to make better-informed decisions for your company regarding generative AI and learn how to build working prototypes quickly. While the focus is on AWS, this book is a great resource for learning generative AI fundamentals and applying these models to real-world applications. Apply generative AI to your business use cases Determine which generative AI models to use based on the task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets Align generative AI models to human values with reinforcement learning from human feedback Use techniques like retrieval-augmented generation to augment your model Explore libraries such as LangChain and React to develop agents and actions Learn about multimodal models such as Stable Diffusion for image and video generation Get hands-on with Amazon Bedrock, the AWS generative AI managed service

Genai On Aws

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Author : Olivier Bergeret
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-19

Genai On Aws written by Olivier Bergeret 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 2025-03-19 with Computers categories.

The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.

Data Science On Aws

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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-07

Data Science On Aws written by Chris Fregly 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 2021-04-07 with Computers categories.

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Generative Ai Powered Assistant For Developers

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Author : Behram Irani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-30

Generative Ai Powered Assistant For Developers 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 2024-08-30 with Computers categories.

Leverage Amazon Q Developer to boost productivity and maximize efficiency by accelerating software development life cycle tasks Key Features First book on the market to thoroughly explore all of Amazon Q Developer’s features Gain an understanding of Amazon Q Developer's capabilities across the software development life cycle through real-world examples Build apps with Amazon Q Developer by auto-generating code in various languages within supported IDEs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany developers face the challenge of managing repetitive tasks and maintaining productivity. This book will help you tackle both these challenges with Amazon Q Developer, a generative AI-powered assistant designed to optimize coding and streamline workflows. This book takes you through the setup and customization of Amazon Q Developer, demonstrating how to leverage its capabilities for auto-code generation, code explanation, and transformation across multiple IDEs and programming languages. You'll learn to use Amazon Q Developer to enhance coding experiences, generate accurate code references, and ensure security by scanning for vulnerabilities. The book also shows you how to use Amazon Q Developer for AWS-related tasks, including solution building, applying architecture best practices, and troubleshooting errors. Each chapter provides practical insights and step-by-step guidance to help you fully integrate this powerful tool into your development process. You’ll get to grips with effortless code implementation, explanation, transformation, and documentation, helping you create applications faster and improve your development experience. By the end of this book, you’ll have mastered Amazon Q Developer to accelerate your software development lifecycle, improve code quality, and build applications faster and more efficiently.What you will learn Understand the importance of generative AI-powered assistants in developers' daily work Enable Amazon Q Developer for IDEs and with AWS services to leverage code suggestions Customize Amazon Q Developer to align with organizational coding standards Utilize Amazon Q Developer for code explanation, transformation, and feature development Understand code references and scan for code security issues using Amazon Q Developer Accelerate building solutions and troubleshooting errors on AWS Who this book is for This book is for coders, software developers, application builders, data engineers, and technical resources using AWS services looking to leverage Amazon Q Developer's features to enhance productivity and accelerate business outcomes. Basic coding skills are needed to understand the concepts covered in this book.

Ultimate Aws Data Engineering

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Author : Rathish Mohan
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-01-23

Ultimate Aws Data Engineering written by Rathish Mohan and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-23 with Computers categories.

Unlock the Power of AWS Data Engineering and Build Smarter Pipelines for Data-Driven Success. Key Features● Gain an in-depth understanding of essential AWS services such as S3, DynamoDB, Redshift, and Glue to build scalable data solutions.● Learn to design efficient, fault-tolerant data pipelines while adhering to best practices in cost management and security. Book DescriptionIn today’s data-driven era, mastering AWS data engineering is key to building scalable, secure pipelines that drive innovation and decision-making. Ultimate AWS Data Engineering is your comprehensive guide to mastering the art of building robust, cost-effective, and fault-tolerant data pipelines on AWS. Designed for data professionals and enthusiasts, this book begins with foundational concepts and progressively explores advanced techniques, equipping you with the skills to tackle real-world challenges. Throughout the chapters, you’ll dive deep into the core principles of data replication, partitioning, and load balancing, while gaining hands-on experience with AWS services like S3, DynamoDB, Redshift, and Glue. Learn to design resilient data architectures, optimize performance, and ensure seamless data transformation—all while adhering to best practices in cost-efficiency and security. Whether you aim to streamline your organization’s data flow, enhance your cloud expertise, or future-proof your career in data engineering, this comprehensive guide offers the practical knowledge and insights you need to succeed. By the end, you will be ready to craft impactful, data-driven solutions on AWS with confidence and expertise. What you will learn● Design scalable data pipelines using core AWS data engineering tools.● Master data replication, partitioning, and sharding techniques on AWS.● Build fault-tolerant architectures with AWS scalability and reliability. Table of Contents1. Unveiling the Secrets of Data Engineering2. Architecting for Scalability: Data Replication Techniques3. Partitioning and Sharding: Optimizing Data Management4. Ensuring Consistency: Consensus Mechanisms and Models5. Balancing the Load: Achieving Performance and Efficiency6. Building Fault-Tolerant Architectures7. Exploring the Realm of AWS Data Storage Services8. Orchestrating Data Flow9. Advanced Data Pipelines and Transformation10. Data Warehousing Demystified11. Visualizing the Unseen12. AWS Machine Learning: Classic AI to Generative AI13. Advanced Data Engineering with AWS.

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

Using Amazon Bedrock

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Author : Renaldi Gondosubroto
language : en
Publisher: John Wiley & Sons
Release Date : 2025-10-21

Using Amazon Bedrock written by Renaldi Gondosubroto 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 2025-10-21 with Computers categories.

A from-scratch roadmap to building generative AI solutions on AWS with Amazon Bedrock In Using Amazon Bedrock: Learn to Architect, Secure and Optimize Generative AI Applications on AWS, accomplished Software Engineer, developer advocate, and AWS Community Builder, Renaldi Gondosubroto, delivers an in-depth walkthrough of Amazon Bedrock, the keystone generative AI service on the Amazon Web Services cloud. Gondosubroto offers a start-to-finish guide of the service and its capabilities, from prompt engineering with foundational models to building applications using the API, working with multimodal models, and fine-tuning. This book provides hands-on instruction on Amazon Bedrock from an experienced developer and AI specialist. It’s packed with real-world code samples, proven best practices, and techniques that result in reliable, secure, and cost-effective generative AI solutions. You’ll also find: Demonstrations of robust governance guardrails and strategies for reducing development time Repeatable, industry-grade frameworks for continuously iterating and optimizing models in production Contemporary updates that incorporate the latest announcements made by Amazon at the re:Invent conference in December 2024 and beyond Perfect for cloud architects, artificial intelligence engineers, and software engineers, Using Amazon Bedrock is an insightful, original, and practical roadmap to generative AI on AWS that explains the AI fundamentals you need to understand to get started in AWS generative AI development and the hands-on techniques you’ll use every day to transform those concepts into efficient, working solutions.

Agentic Ai For Engineers

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Author : Dhivya Nagasubramanian
language : en
Publisher: Apress
Release Date : 2026-05-18

Agentic Ai For Engineers written by Dhivya Nagasubramanian and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-05-18 with Computers categories.

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems. The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built. By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. What You Will Learn A practical introduction to Machine Learning and Generative AI, tailored for engineers Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background. The core principles of Agentic AI, including goals, environments, actions, and feedback loops Understand different Agentic AI frameworks and their applications. Integrate agentic systems into real-world applications using hands-on coding examples Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments Who This Book Is For Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.