E2E Automation from JENKINS to LISA

Ram Aurovind

Abstract: The CLIENT_MICROSERVICE Trade Tools team's initiative, E2EAUTOMATION, stands out as a forward-thinking response to the persistent inefficiencies in international CRM, SRM, shipping, and banking systems. Rather than following traditional, error-prone methods, the project proposes an intelligent, microservices-driven platform capable of classifying commodities and accurately predicting duties, taxes, and transit times all in just one click. This isn?t merely a technical shift; it signals a deeper movement toward data democratization and operational agility. The fusion of Big Data pipelines, machine learning, and parameterized Jenkins pipelines has streamlined workflows, drastically cut down manual coding and approvals, and introduced reusable, modular architectures. What?s particularly noteworthy is how the team tackled real pain points fragmented development, delayed testing, and complex deployments by consolidating job executions, reducing pipeline code by over 60%, and implementing a shared Jenkins library for scalable, consistent integrations. This suggests that E2EAUTOMATION isn?t just about code efficiency; it?s about reimagining how automation can elevate the entire user journey, from back-end logic to real-time decision-making. It is evident that this architecture offers a replicable blueprint for any enterprise aiming to modernize their trade and logistics operations while ensuring traceability, governance, and rapid deployment.

Keywords: microservices automation, intelligent pipeline, Jenkins shared library, Big Data integration, commodity classification