Review of Data Engineering Frameworks (Trino and Kubernetes) for Implementing Generative AI in Financial Risk

This paper is discusses Data Engineering Full Stack Framework for using Generative AI (GenAI) in financial risk management.We have discusses challenges, proposals and application of various data tools directly applicable for implementation of Gen AI in Financial risk.We have discussed issues and comparison of tools for data engineering pertinent to Generative AI (GenAI) is transforming financial risk management by leveraging Gen AI models to generate synthetic data for stress testing, anomaly detection, and risk modeling.This paper explores full-stack big data frameworks, including Apache Spark, Trino, Kubernetes, AWS SageMaker, and Databricks, to deploy GenAI models at scale.This paper furthur provides a literature review of AI and Generative AI applications in Trino, discussing query performance, optimization techniques, and AI-driven analytics.Additionally, we explore the bidirectional synergy between Trino and Generative AI-how AI enhances Trino's capabilities and how Trino facilitates advanced AI workloads.Key contributions include insights from recent research on Trino's scalability, AI-powered query execution, and the trade-offs between generative models and computational efficiency.Deployment strategies leveraging AWS, Kubernetes, and Apache Spark for scalable risk computation are also discussed.Generative AI (GenAI) is transforming financial risk management by leveraging deep learning models to generate synthetic data for stress testing, anomaly detection, and risk modeling.This paper also reviews the latest developments in Azure-based AI agents and Kubernetes orchestration for AI/ML workloads.We categorize recent advancements, including agentic workflows, AI/ML orchestration, Kubernetes integration, and AI-driven DevOps.