Scalable Real-Time Market Data Processing Architecture for High-Volume Multi-Asset Analytics in Fund Management

Authors

  • Phaneendra Vayu Kumar Yerra

Keywords:

Real-time data processing, high-frequency trading infrastructure, multi-asset analytics, Apache Kafka, stream processing, fund portfolio management, market data latency, data pipeline scalability, HTAP systems, risk management

Abstract

This research paper examines architectural design and operational requirements for scalable real-time market data processing systems serving fund management at enterprise scale. Global financial markets generate 147 zettabytes of data annually with real-time quote updates exceeding 2.1 million per second in 2024, creating unprecedented integration challenges across heterogeneous data sources. The paper synthesizes infrastructure patterns, comparing Lambda, Kappa, and HTAP architectures through empirical benchmarking and cost analysis. Critical findings indicate Kappa architectures achieve 50-200 millisecond end-to-end latencies with operational simplicity, while HTAP systems deliver 10-100 millisecond query response times. Market data infrastructure costs range from USD 6.8 million annually for USD 1-10 billion AUM funds to USD 55 million for institutions exceeding USD 50 billion AUM. Research demonstrates horizontally scalable microservices enable processing of 5.8 terabytes daily market data, supporting 620 portfolio rebalancing events daily. Industry spending reaches USD 44.3 billion globally in 2024, growing 6.4 percent annually..

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References

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Published

30.12.2024

How to Cite

Phaneendra Vayu Kumar Yerra. (2024). Scalable Real-Time Market Data Processing Architecture for High-Volume Multi-Asset Analytics in Fund Management. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 3954 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7973

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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