Preface xv
Devasis PRADHAN
Acknowledgments xvii
Chapter 1. Real-Time Big Data Processing in the Cloud: Scalable, Cost-Efficient, AI-Driven Solutions for Financial Analytics 1
Saravanan Thirumazhisai PRABHAGARAN, Ishu Anand JAISWAL and Hina GANDHI
1.1. Introduction 1
1.2. The evolution of big data in finance 2
1.3. Architectural components of real-time analytics 3
1.4. Real-time ingestion and stream processing 4
1.5. Cost optimization strategies in cloud infrastructure 6
1.6. Case studies and applications in financial analytics 8
1.7. How data enters the pipeline 9
1.8. Processing in real-time 9
1.9. Storing the results 9
1.10. Scaling without blowing the budget 10
1.11. A few examples 10
1.12. Additional implementation stories 11
1.13. Common pitfalls and how to avoid them 14
1.14. Lessons learned 14
1.15. Where the model sits in the pipeline 15
1.16. Example: fraud model in production 15
1.17. How teams deploy models 16
1.18. Retraining and model drift 16
1.19. Latency considerations 17
1.20. A few more lessons 19
1.21. Why people do it anyway 19
1.22. What usually goes wrong 19
1.23. How some teams made it work 20
1.24. A few stories 21
1.25. Conclusion 23
1.26. References 23
Chapter 2. Scalable Microservices and Data Integration for Modern eCommerce and Enterprise Systems 27
Aneeshkumar P. SUNDESWARAN, Dilip Prakash VALANARASU and Anaswara Thekkan RAJAN
2.1. Introduction 28
2.2. Literature review 32
2.3. Methodology 35
2.4. Results 42
2.5. Conjunction with former work 45
2.6. Future direction 46
2.7. Conclusion 48
2.8. References 51
Chapter 3. Modern Data Engineering on GCP: Scalable Pipelines, Real-Time Processing and Governance 55
Jagadeesh THIRUVEEDULA and Souvari Ranjan BISWAL
3.1. Introduction 55
3.2. Designing scalable pipelines 57
3.3. Real-time data processing 58
3.4. Data governance and compliance 59
3.5. Case study: real-time fraud detection 61
3.6. Advanced patterns and best practices 61
3.7. Monitoring and observability 63
3.8. Cost optimization strategies 63
3.9. ML integration 64
3.10. Looking ahead 64
3.11. Final thoughts 65
3.12. References 65
Chapter 4. AI for Personalization and Security in Digital Travel Platforms 67
Kartheek DOKKA, Saravanan Thirumazhisai PRABHAGARAN and Maheswari GOVINDARAJU
4.1. Introduction 67
4.2. Evolution of AI in travel and digital services 70
4.3. AI-driven personalization techniques 74
4.4. AI in platform and transactional security 76
4.5. Integration of AI in travel platforms 78
4.6. Challenges and ethical considerations 81
4.7. Future outlook and strategic implications 85
4.8. Conclusion 87
4.9. References 88
Chapter 5. Modernizing Legacy E-commerce Platforms: Cloud Migration, DevOps and API-First Strategies 91
Dilip Prakash VALANARASU and Arnab KAR
5.1. Introduction 92
5.2. Understanding legacy e-commerce architectures 94
5.3. Cloud migration strategies for legacy systems 97
5.4. DevOps as a catalyst for e-commerce agility 101
5.5. API-first strategy: modularizing e-commerce architectures 105
5.6. Security, observability and compliance in modern platforms 108
5.7. Strategic roadmap and future directions 111
5.8. Conclusion 113
5.9. References 114
Chapter 6. Building Scalable and Resilient Stream Processing Systems with Kafka and Serverless Tools 119
Vamsi Krishna KOGANTI and Naveen Saikrishna PUPPALA
6.1. Introduction: the evolution of stream processing in cloud-native systems 119
6.2. Apache Kafka: core principles and its role in stream ingestion 121
6.3. Serverless computing paradigms in stream processing 123
6.4. Architecting scalable stream processing pipelines 126
6.5. Resilience and fault tolerance in distributed stream systems 129
6.6. Performance optimization, observability and cost management 132
6.7. Conclusion: future directions for serverless and Kafka-based architectures 134
6.8. References 136
Chapter 7. AI-Driven Automation for Scalable Media, Data and Search Systems 139
Aatishkumar DHAMI and Maheswari GOVINDARAJU
7.1. Introduction 139
7.2. The rise of automated data pipelines 141
7.3. Scaling media processing without losing control 142
7.4. The art and trouble of real-time search systems 143
7.5. Building robust, automated data pipelines 147
7.6. Machine learning in analytics and personalization 148
7.7. Monitoring and keeping systems trustworthy 149
7.8. Training methods for sorting 151
7.9. Checks and alerts 152
7.10. Manual controls and overrides 152
7.11. Monitoring and cost controls 154
7.12. Fast updates and small delays 155
7.13. Checks that made a difference 155
7.14. When costs rose fast 156
7.15. Adjustments made during high-volume events 156
7.16. Conclusion 158
7.17. References 158
Chapter 8. Scalable and Cost-Efficient Sap Cloud Migration and Operations for Large Enterprises 163
Ankit Kumar GUPTA
8.1. Introduction 163
8.2. Background and related work 166
8.3. Drivers and benefits of SAP cloud migration 170
8.4. Migration planning framework 172
8.5. Migration execution strategies 174
8.6. Post-migration optimization 175
8.7. Case studies 179
8.8. Challenges, risks and future directions 181
8.9. Conclusion 184
8.10. References 184
Chapter 9. Integrative Morphological Assessment of Ayurvedic Haritaki (Terminalia chebula Retz.) Varieties: A Comparative Study Across Indian Regions and the Western Ghats 191
Komal TAYADE, Elingbam SINGH, Varsha GHATE, Suresh JAGTAP and Rakshanda PATIL
9.1. Introduction 192
9.2. Materials and methods 194
9.3. Results and discussion 197
9.4. Conclusion 205
9.5. References 205
Chapter 10. Smart Analytics in SAP: Streamlining Big Data for Faster, Smarter Business Decisions 207
Vaidheyar Raman BALASUBRAMANIAN
10.1. Introduction 208
10.2. Applications and use cases across industries 212
10.3. Tools, functionalities and AI/ML integration in SAP smart analytics 215
10.4. Implementation challenges and best practices for SAP smart analytics 219
10.5. Future scope 223
10.6. Conclusion 226
10.7. References 226
Chapter 11. AI-Powered Feedback Loops in Digital Advertising 229
Divij PASRIJA
11.1. Introduction 229
11.2. Understanding feedback loops in digital advertising 232
11.3. Role of AI in enhancing feedback mechanisms 237
11.4. Architectures and models for AI-driven ad feedback loops. 241
11.5. Real-world applications 244
11.6. Challenges, ethical considerations and future directions 248
11.7. Conclusion 251
11.8. References 251
List of Authors 257
Index 261
