Most cloud programs are incomplete unless both data and applications are considered, yet data modernization is often an afterthought. While data modernization is more invasive, it can deliver high ROI if executed properly. Traditional data solutions are typically monolithic with long release cycles, create single points of failure, and require expensive proprietary hardware and licenses.
While implementing Spring-based microservices running on the Pivotal Platform, even data must be decomposed for optimal performance and cost.
In this session, we'll demonstrate how individual Spring Cloud components of a data injection process can be scaled independently, as compared to traditional data injection tools and components that require the entire stack to be scaled up together. We'll also examine a typical use case, and show how a balanced consultative approach and automation-driven migration strategy can address the issues that arise during such programs.