Observability in Finance

This book explains the role of observability in the finance sector, showing how new technologies can help monitor and manage financial systems more effectively. It outlines the use of real-time data monitoring, Machine Learning, and cloud computing to enhance the efficiency of financial operations and ensure they meet regulatory standards.

The chapters guide you through the process of setting up systems to track financial activities accurately, analyze market trends, and predict future challenges to keep operations secure and competitive. It offers clear explanations of how these technologies can help finance professionals make better decisions and manage risks proactively.

Designed for finance professionals looking to update their technical skills, this book provides practical guidance on adopting modern observability tools and practices. It will help you understand how to apply these technologies to increase transparency and strengthen the resilience of financial operations in a constantly evolving industry.

KEY FEATURES  

• Learn observability basics in finance.
• Monitor financial data with logs and alerts and improve data security.
• Identify the key metrics for financial oversight.
• Use new tech for financial observability.

WHAT YOU WILL LEARN

• Implement effective data monitoring strategies in finance.
• Use Machine Learning to enhance financial risk assessment.
• Develop robust compliance frameworks using observability tools.
• Apply real-time analytics for quicker financial decision-making.
• Integrate predictive analytics for forward-looking financial insights.
• Understand and deploy distributed tracing for financial operations.

WHO THIS BOOK IS FOR

This book is ideal for financial professionals seeking to deepen their understanding of observability. It is also suitable for IT specialists in finance who wish to advance their skills in modern observability tools and practices. 

1. Introduction
2. The Fundamentals of Observability 
3. Monitoring and Logging for Financial Data
4. Tracing and Correlation in Finance
5. Metrics and Key Performance Indicators for Finance
6. Real-time Monitoring and Alerting in Finance
7. Observability for Algorithmic Trading and Market Data
8. Compliance and Regulatory Considerations
9. Advanced Techniques: Machine Learning and Predictive Analytics
10. Organizational Culture and Collaboration
11. Case Studies and Best Practices Observability
12. The Future of Observability in Finance
13. The Horizon of Financial Observability

Brindha Priyadarshini Jeyaraman is a data science and engineering leader with over 14 years of experience, holding a Master’s in Knowledge Engineering from the National University of Singapore. She’s currently advancing her education with a Doctor of Engineering in Financial Knowledge Graphs at Singapore Management University. She currently works as a Principal architect for AI at Google Cloud and is proficient in building complex machine learning systems using technologies like Python, Java, R, Spark, and Kafka. She excels in agile development environments and has extensive experience with ML Ops and architecting solutions on AWS and Google Cloud. She has authored two books on real-time streaming and machine learning, where she shares her knowledge on building analytical platforms and developing practical machine learning applications. Her technical certifications include Sun Certified Java Programmer (SCJP), Sun Certified Web Component Developer (SCWCD), and DB2 Certified. She actively contributes to the field through research and speaking at industry conferences. Her professional journey includes impactful roles at Google, Monetary Authority of Singapore, and I2R handling projects across telecommunications, finance, e-commerce, and research. Brindha’s leadership and expertise make her prominent in the field of data science and engineering, significantly contributing to advancements in these domains.