Microservices And Automation Excellence Full Stack Development For The Intelligent Enterprise 2025
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Microservices and Automation Excellence: Full-Stack Development for the Intelligent Enterprise 2025
Author: AUTHOR:1-VAMSI KRISNA KONDREDDY, AUTHOR:2-DR DEEPENDRA RASTOGI
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In an era defined by agility, intelligence, and automation, modern software architectures have undergone a dramatic transformation. Enterprises are increasingly moving beyond monolithic systems to embrace microservices, serverless functions, and event-driven platforms. This paradigm shift is not only architectural but deeply cultural—blending DevOps philosophies, AI-driven insights, and cloud-native technologies to create intelligent, self-optimizing ecosystems. Microservices and Automation Excellence: Full-Stack Development for the Intelligent Enterprise is the result of years of research, practical experience, and emerging trends observed across industries—from finance and healthcare to logistics and retail. This book was written with a singular goal: to provide a structured, practitioner-focused roadmap for engineers, architects, and leaders who are building the next generation of distributed, scalable, and intelligent applications. Across twelve comprehensive chapters, we explore a wide spectrum of topics—ranging from the fundamentals of microservices and CI/CD pipelines to innovative concepts like AIOps, platform engineering, and edge-native microservices. Each chapter is designed to be modular, enriched with real-world examples, industry case studies, tool comparisons, and the latest insights from the field. Whether you’re an architect designing resilient systems, a DevOps engineer automating delivery pipelines, or a product leader aligning technology with business goals, this book aims to serve as both a foundational guide and a forward-looking companion. We hope this work not only informs your decisions but also inspires new thinking around how to build and operate software in a world increasingly powered by intelligence, autonomy, and scale. In addition, the book addresses the ethical challenges and privacy concerns associated with voice recognition technologies. While the potential for these tools is vast, they raise important questions about data security, user consent, and the potential for misuse. As we look to the future, we must balance innovation with responsibility to ensure that these technologies serve the broader interests of society. The purpose of this book is not only to present the current state of the art in generative AI for voice recognition but also to offer a forward-looking perspective on the trends and research directions that will shape the next generation of voice-enabled applications. With emerging technologies such as neural text-to-speech (TTS), multilingual speech models, and real-time voice translation, the possibilities seem limitless, and the need for understanding these advanced AI applications is more pressing than ever. Whether you are a researcher seeking a deep understanding of generative AI in voice recognition or a developer looking for practical knowledge to build the next big voice-enabled application, this book aims to provide the knowledge and insights needed to navigate this exciting and transformative field. The world of voice recognition is evolving at an astonishing pace, and with the help of generative AI, we are only beginning to scratch the surface of its potential. Join us on this journey through the realm of voice recognition and generative AI, where we will explore the technologies, applications, and innovations that are defining the future of human-computer interaction. Authors Vamsi Krisna Kondreddy Dr Deependra Rastogi
Refactoring
Author: Martin Fowler
language: en
Publisher: Addison-Wesley Professional
Release Date: 1999
Refactoring is gaining momentum amongst the object oriented programming community. It can transform the internal dynamics of applications and has the capacity to transform bad code into good code. This book offers an introduction to refactoring.
Data Engineering for AI
Author: Sundeep Goud Katta
language: en
Publisher: BPB Publications
Release Date: 2025-06-26
DESCRIPTION Data engineering is the critical discipline of building and maintaining the systems that enable organizations to collect, store, process, and analyze vast amounts of data, especially for advanced applications like AI and ML. It is about ensuring that it is reliable, accessible, and high-quality for everyone who needs it. This book provides a thorough exploration of the complete data lifecycle, starting with data engineering's development and its vital link to AI. It provides an overview of scalable data practices, from legacy systems to cutting-edge techniques. The reader will explore real-time data collection, secure ingestion, optimized storage, and dynamic processing techniques. The book features detailed discussions on ETL and ELT frameworks, performance tuning, and quality assurance that are complemented by real-world case studies. All these empower the data engineers to design systems that are seamless and integrate well with AI pipelines, driving innovation across diverse industries. By the end of this book, readers will be well-equipped to design, implement, and manage scalable data engineering solutions that effectively support and drive AI initiatives within any organization. WHAT YOU WILL LEARN ● Design real-time data ingestion and processing systems. ● Implement optimized data storage solutions for AI workloads. ● Ensure data quality, compliance in dynamically changing environments. ● Build scalable data collection methods, including for AI training data. ● Apply data engineering solutions in complex, real-world AI projects. ● Conduct SQL analytics and craft insightful, AI-driven visualizations. WHO THIS BOOK IS FOR This book is for data engineers, AI practitioners, and curious professionals with a foundational understanding of databases, programming, and ETL processes. A basic understanding of computer science concepts, cloud computing, and analytics is helpful. TABLE OF CONTENTS 1. Introduction to Data Engineering in AI 2. Managing Data Collection 3. Data Ingestion in Action 4. Data Storage in Real-time 5. Data Processing Techniques and Best Practices 6. Data Integration and Interoperability 7. Ensuring Data Quality 8. Understanding Data Analytics 9. Data Visualization and Reporting 10. Operational Data Security 11. Protecting Data Privacy 12. Data Engineering Case Studies