The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.

Real-time data processing, Apache Kafka, Apache Spark, Financial microservices, Generative AI (LLMs), Cloud-native architecture.

Acharya, A., & Sidnal, N. S. (2016, December). High frequency trading with complex event processing. In 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW) (pp. 39-42). IEEE.

Aguoru, K. C. (2015). An Empirical Investigation of the Causes and Consequences of Card-Not-Present Fraud, Its Impact and Solution (Doctoral dissertation, University of East London).

Ahuja, A. (2024). A Detailed Study on Security and Compliance in Enterprise Architecture.

Alam, M. A., Nabil, A. R., Mintoo, A. A., & Islam, A. (2024). Real-Time Analytics In Streaming Big Data: Techniques And Applications. Journal of Science and Engineering Research, 1(01), 104-122.

Aldridge, I., & Krawciw, S. (2017). Real-time risk: What investors should know about FinTech, high-frequency trading, and flash crashes. John Wiley & Sons.

Armbrust, M., Das, T., Torres, J., Yavuz, B., Zhu, S., Xin, R., ... & Zaharia, M. (2018, May). Structured streaming: A declarative api for real-time applications in apache spark. In Proceedings of the 2018 International Conference on Management of Data (pp. 601-613).

Asimiyu, Z. (2023). Scalable Inference Systems for Real-Time LLM Integration.

Awan, A. J., Brorsson, M., Vlassov, V., & Ayguade, E. (2016, October). Micro-architectural characterization of apache spark on batch and stream processing workloads. In 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom) (pp. 59-66). IEEE.

Bejeck, B. (2024). Kafka Streams in Action: Event-driven Applications and Microservices. Simon and Schuster.

Bird, D. A. (Ed.). (2020). Real-time and retrospective analyses of cyber security. IGI Global.

Casalicchio, E., & Iannucci, S. (2020). The state‐of‐the‐art in container technologies: Application, orchestration and security. Concurrency and Computation: Practice and Experience, 32(17), e5668.

Celar, S., Mudnic, E., & Seremet, Z. (2017). State-of-the-art of messaging for distributed computing systems. Vallis Aurea, 3(2), 5-18.

Chandra, A., Moen, S., & Sellers, C. (2016). What role does the private sector have in supporting disaster recovery, and what challenges does it face in doing so?. Santa Monica, CA: Rand Corporation.

Chavan, A. (2023). Managing scalability and cost in microservices architecture: Balancing infinite scalability with financial constraints. Journal of Artificial Intelligence & Cloud Computing, 2, E264. http://doi.org/10.47363/JAICC/2023(2)E264

Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and reliability. Journal of Computer Science and Technology Studies, 6(2), 183-198. https://doi.org/10.32996/jcsts.2024.6.2.21

Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of network and computer applications, 128, 105-140.

Eldon, L., & Kondakhchyan, A. (2018). Introducing information communication technologies into humanitarian programming.

Emma, O. T., & Peace, P. (2023). Building an Automated Data Ingestion System: Leveraging Kafka Connect for Predictive Analytics.

Friedman, E., & Tzoumas, K. (2016). Introduction to Apache Flink: stream processing for real time and beyond. " O'Reilly Media, Inc.".

Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies. International Journal of Science and Research Archive, 13(2), 2155. https://doi.org/10.30574/ijsra.2024.13.2.2155

Goldman, E. (2021). Content moderation remedies. Mich. Tech. L. Rev., 28, 1.

Joy, N. (2024). Scalable Data Pipelines for Real-Time Analytics: Innovations in Streaming Data Architectures. International Journal of Emerging Research in Engineering and Technology, 5(1), 8-15.

Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal of Economics & Business. https://www.ashwinanokha.com/ijeb-v22-4-2023.php

Khan, A. (2017). Key characteristics of a container orchestration platform to enable a modern application. IEEE cloud Computing, 4(5), 42-48.

Khlaaf, H., Mishkin, P., Achiam, J., Krueger, G., & Brundage, M. (2022). A hazard analysis framework for code synthesis large language models. arXiv preprint arXiv:2207.14157.

Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Lin, T. C. (2016). The new market manipulation. Emory LJ, 66, 1253.

Mishra, M., Sidoti, D., Avvari, G. V., Mannaru, P., Ayala, D. F. M., Pattipati, K. R., & Kleinman, D. L. (2017). A context-driven framework for proactive decision support with applications. IEEE Access, 5, 12475-12495.

Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: the definitive guide: real-time data and stream processing at scale. " O'Reilly Media, Inc.".

Norrhall, S. P. K. (2018). Continuous queries on streaming data (Master's thesis, NTNU).

Pasha, M. H. M. (2024). Harnessing Artificial Intelligence to Build Agile and Resilient Business Ecosystems for the Smart Economy of the Future. Journal of Business and Future Economy, 1(2), 31-40.

Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf

Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3), 145-164.

Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient

Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30-39.

Sidharth, S. (2019). Enhancing Security of Cloud-Native Microservices with Service Mesh Technologies.

Singh, V. (2022). Advanced generative models for 3D multi-object scene generation: Exploring the use of cutting-edge generative models like diffusion models to synthesize complex 3D environments. https://doi.org/10.47363/JAICC/2022(1)E224

Sukhadiya, J., Pandya, H., & Singh, V. (2018). Comparison of Image Captioning Methods. INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH, 6(4), 43-48. https://rjwave.org/ijedr/papers/IJEDR1804011.pdf

Sulkava, A. (2023). Building scalable and fault-tolerant software systems with Kafka.

Von Solms, J. (2021). Integrating Regulatory Technology (RegTech) into the digital transformation of a bank Treasury. Journal of Banking Regulation, 22(2), 152-168.