Real-Time Financial Data Processing Using Apache Spark and Kafka

Аннотация

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