Java Web Development (JSP/Servlets) Services

Enterprise Java persistence has never really been about APIs. It has always been about assumptions. Long before frameworks, annotations, or repositories entered the picture, the enterprise Java ecosystem was shaped by a single, dominant belief: persistence meant relational databases. That assumption influenced how applications were designed, how teams reasoned about data, and how the Java platform itself evolved.

This article is inspired by a presentation given by Arjan Tijms, director of OmniFish, titled “Next-level persistence in Jakarta EE: Jakarta Data and Jakarta NoSQL.” Delivered in 2024, the talk offers a clear and pragmatic view of why Jakarta EE persistence needed to evolve, how Jakarta Data fits into the platform, and how it relates to Jakarta Persistence and Jakarta NoSQL. While the presentation provides the technical backbone, this article expands on the historical context and architectural motivations behind that evolution.

Java has become one of the world’s most versatile programming languages, chosen for its adaptability, stability, and platform independence. Its extensive ecosystem encompasses virtually every application type, from web development to enterprise solutions, game design, the Internet of Things (IoT), and beyond.

With an estimated 51 billion active Java Virtual Machines (JVMs) globally, it goes without question that Java powers a substantial portion of modern software infrastructure.

Most teams add rate limiting after an outage, not before one. I’ve done it both ways, and the “after” version usually looks like this: someone picks a number (say 500 rps), wires up a filter, and feels safer. Then the next incident happens anyway — because the problem wasn’t the number.

The real problems tend to be:

The recent announcement that Global Payments will acquire Worldpay for $22.7 billion has once again put the spotlight on the payment processing space. This move consolidates two giants and signals the growing importance of real-time, global payment infrastructure. But behind this shift is something deeper: data streaming has become the backbone of modern payment systems.

From Stripe’s 99.9999% Kafka availability to PayPal streaming over a trillion events per day, and Payoneer replacing its existing message broker with data streaming, the world’s leading payment processors are redesigning their core systems around streaming technologies. Even companies like Worldline, which developed its own Apache Kafka management platform, have made Kafka central to their financial infrastructure.

Agentic AI is changing how enterprises think about automation and intelligence. Agents are no longer reactive systems. They are goal-driven, context-aware, and capable of autonomous decision-making. But to operate effectively, agents must be connected to the real-time pulse of the business. This is where data streaming with Apache Kafka and Apache Flink becomes essential.

Apache Flink is entering a new phase with the proposal of Flink Agents, a sub-project designed to power system-triggered, event-driven AI agents natively within Flink’s streaming runtime. Let’s explore what this means for the future of agentic systems in the enterprise.