Devasis Pradhan is the Assistant Director of Research at Acharya Institutes, and the Dean of Research at the Acharya Institute of Technology, Bangalore, India. His research specializes in AI, communication networks, computational intelligence, data analytics and emerging digital technologies.Hla Myo Tun is the Rector of Yangon Technological University, Myanmar. His research specializes in intelligent systems, engineering technologies, data analytics and next-generation communication infrastructures.David Banjerdpongchai is a Professor at Chulalongkorn University, Thailand. His research specializes in advanced computing, AI, smart technologies and interdisciplinary engineering applications.Prasenjit Chatterjee is a Professor and Dean (Research and Consultancy) at MCKV Institute of Engineering, India. His expertise specializes in decision analytics, sustainable systems, operations management and AI-driven engineering innovation.

• Preface xvDevasis PRADHANAcknowledgments xviiChapter 1. Real-Time Big Data Processing in the Cloud: Scalable, Cost-Efficient, AI-Driven Solutions for Financial Analytics 1Saravanan Thirumazhisai PRABHAGARAN, Ishu Anand JAISWAL and Hina GANDHI1.1. Introduction 11.2. The evolution of big data in finance 21.3. Architectural components of real-time analytics 31.4. Real-time ingestion and stream processing 41.5. Cost optimization strategies in cloud infrastructure 61.6. Case studies and applications in financial analytics 81.7. How data enters the pipeline 91.8. Processing in real-time 91.9. Storing the results 91.10. Scaling without blowing the budget 101.11. A few examples 101.12. Additional implementation stories 111.13. Common pitfalls and how to avoid them 141.14. Lessons learned 141.15. Where the model sits in the pipeline 151.16. Example: fraud model in production 151.17. How teams deploy models 161.18. Retraining and model drift 161.19. Latency considerations 171.20. A few more lessons 191.21. Why people do it anyway 191.22. What usually goes wrong 191.23. How some teams made it work 201.24. A few stories 211.25. Conclusion 231.26. References 23Chapter 2. Scalable Microservices and Data Integration for Modern eCommerce and Enterprise Systems 27Aneeshkumar P. SUNDESWARAN, Dilip Prakash VALANARASU and Anaswara Thekkan RAJAN2.1. Introduction 282.2. Literature review 322.3. Methodology 352.4. Results 422.5. Conjunction with former work 452.6. Future direction 462.7. Conclusion 482.8. References 51Chapter 3. Modern Data Engineering on GCP: Scalable Pipelines, Real-Time Processing and Governance 55Jagadeesh THIRUVEEDULA and Souvari Ranjan BISWAL3.1. Introduction 553.2. Designing scalable pipelines 573.3. Real-time data processing 583.4. Data governance and compliance 593.5. Case study: real-time fraud detection 613.6. Advanced patterns and best practices 613.7. Monitoring and observability 633.8. Cost optimization strategies 633.9. ML integration 643.10. Looking ahead 643.11. Final thoughts 653.12. References 65Chapter 4. AI for Personalization and Security in Digital Travel Platforms 67Kartheek DOKKA, Saravanan Thirumazhisai PRABHAGARAN and Maheswari GOVINDARAJU4.1. Introduction 674.2. Evolution of AI in travel and digital services 704.3. AI-driven personalization techniques 744.4. AI in platform and transactional security 764.5. Integration of AI in travel platforms 784.6. Challenges and ethical considerations 814.7. Future outlook and strategic implications 854.8. Conclusion 874.9. References 88Chapter 5. Modernizing Legacy E-commerce Platforms: Cloud Migration, DevOps and API-First Strategies 91Dilip Prakash VALANARASU and Arnab KAR5.1. Introduction 925.2. Understanding legacy e-commerce architectures 945.3. Cloud migration strategies for legacy systems 975.4. DevOps as a catalyst for e-commerce agility 1015.5. API-first strategy: modularizing e-commerce architectures 1055.6. Security, observability and compliance in modern platforms 1085.7. Strategic roadmap and future directions 1115.8. Conclusion 1135.9. References 114Chapter 6. Building Scalable and Resilient Stream Processing Systems with Kafka and Serverless Tools 119Vamsi Krishna KOGANTI and Naveen Saikrishna PUPPALA6.1. Introduction: the evolution of stream processing in cloud-native systems 1196.2. Apache Kafka: core principles and its role in stream ingestion 1216.3. Serverless computing paradigms in stream processing 1236.4. Architecting scalable stream processing pipelines 1266.5. Resilience and fault tolerance in distributed stream systems 1296.6. Performance optimization, observability and cost management 1326.7. Conclusion: future directions for serverless and Kafka-based architectures 1346.8. References 136Chapter 7. AI-Driven Automation for Scalable Media, Data and Search Systems 139Aatishkumar DHAMI and Maheswari GOVINDARAJU7.1. Introduction 1397.2. The rise of automated data pipelines 1417.3. Scaling media processing without losing control 1427.4. The art and trouble of real-time search systems 1437.5. Building robust, automated data pipelines 1477.6. Machine learning in analytics and personalization 1487.7. Monitoring and keeping systems trustworthy 1497.8. Training methods for sorting 1517.9. Checks and alerts 1527.10. Manual controls and overrides 1527.11. Monitoring and cost controls 1547.12. Fast updates and small delays 1557.13. Checks that made a difference 1557.14. When costs rose fast 1567.15. Adjustments made during high-volume events 1567.16. Conclusion 1587.17. References 158Chapter 8. Scalable and Cost-Efficient Sap Cloud Migration and Operations for Large Enterprises 163Ankit Kumar GUPTA8.1. Introduction 1638.2. Background and related work 1668.3. Drivers and benefits of SAP cloud migration 1708.4. Migration planning framework 1728.5. Migration execution strategies 1748.6. Post-migration optimization 1758.7. Case studies 1798.8. Challenges, risks and future directions 1818.9. Conclusion 1848.10. References 184Chapter 9. Integrative Morphological Assessment of Ayurvedic Haritaki (Terminalia chebula Retz.) Varieties: A Comparative Study Across Indian Regions and the Western Ghats 191Komal TAYADE, Elingbam SINGH, Varsha GHATE, Suresh JAGTAP and Rakshanda PATIL9.1. Introduction 1929.2. Materials and methods 1949.3. Results and discussion 1979.4. Conclusion 2059.5. References 205Chapter 10. Smart Analytics in SAP: Streamlining Big Data for Faster, Smarter Business Decisions 207Vaidheyar Raman BALASUBRAMANIAN10.1. Introduction 20810.2. Applications and use cases across industries 21210.3. Tools, functionalities and AI/ML integration in SAP smart analytics 21510.4. Implementation challenges and best practices for SAP smart analytics 21910.5. Future scope 22310.6. Conclusion 22610.7. References 226Chapter 11. AI-Powered Feedback Loops in Digital Advertising 229Divij PASRIJA11.1. Introduction 22911.2. Understanding feedback loops in digital advertising 23211.3. Role of AI in enhancing feedback mechanisms 23711.4. Architectures and models for AI-driven ad feedback loops. 24111.5. Real-world applications 24411.6. Challenges, ethical considerations and future directions 24811.7. Conclusion 25111.8. References 251List of Authors 257Index 261