Generative AI DevOps in Real-Time Finance
Keywords:
Generative Artificial Intelligence, GenAI Enabled DevOps, Real Time Transaction Processing, Financial Services Pipelines, Black Swan Event Handling, Enterprise Architecture Theory, Cloud Native Architectures, Transaction Pipeline Scalability, DevOps Automation Practices, Real Time Data Processing, Operational Resilience Engineering, Non Functional Requirements Analysis, Intelligent Pipeline Monitoring, Adaptive System Design, Financial Infrastructure Modernization, Event Driven Architectures, High Volume Transaction Systems, AI Assisted Software Operations, Architectural Patterns And Trade Offs, Enterprise Grade Transaction Systems.Abstract
The emergence of Generative AI (GenAI) unlocks new capabilities in software development and operations. A related trend calls for the adoption of DevOps principles and practices. Furthermore, organizations across industries seek to process data in real time. In financial services, that imperative applies to transaction pipelines, where volumes peak at Black Swan events, yet the average workload is relatively modest. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. The landscape of existing work is therefore reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions that reconcile GenAI-enabled DevOps principles with the requirements of real-time transaction processing. A foundation for that exploration is laid by: specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial Pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs.
Real-time transaction processing pipelines in financial services must support Black Swan event volumes while meeting operational requirements at lower loads. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. Existing work is reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions reconciling GenAI-enabled DevOps principles with the requirements of real-time financial transaction processing. A foundation for that exploration is laid by specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs.
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