Java Web Development (JSP/Servlets) Services

JSP might be old, not fancy, or trendy anymore, but many legacy systems still use it, and there are development teams tasked with maintaining and extending systems with a JSP frontend (see https://webtechsurvey.com/technology/javaserver-pages). What can you do when you need to work on a code base that has unit tests for the Java code, but a significant part of the code base is living in (an untested) frontend code and is prone to failures? 

You can rely on code reviews or pull requests, but that seems insufficient to flag even trivial issues. You can wait for manual testers or automated UI tests to find problems after the change was deployed to the QA environment, but that is way too late and cumbersome.

At-least-once delivery is treated like a safety net in Java event systems. Nothing gets lost. Retries handle failures. Duplicates are “a consumer problem.” It sounds practical, even mature.

That assumption doesn’t survive production.

If you are working with data pipelines, chances are you have crossed paths with Apache NiFi. For years, it's been the go-to way for getting data from point A to point B (and often C, D, and E). Its visual interface makes building complex routing, transformation, and delivery flows surprisingly easy, handling everything from simple log collection to intricate IoT data streams across countless organizations. It's powerful, it's flexible, and honestly, it just works really well for shuffling bits around reliably. We set up our sources, connect our processors, define our destinations, and watch the data flow — job done, right?

AI Opportunity

Well, mostly. While Apache NiFi is fantastic at the logistics of data movement, I started wondering: what if we could make the data smarter while it's still in motion? We hear about AI everywhere, crunching massive datasets after they've landed in a data lake or warehouse. But what about adding that intelligence during ingestion? Imagine enriching events, making routing decisions based on predictions, or flagging anomalies before the data even hits its final storage.

There is no doubt that nowadays software applications and products that have a significant contribution to our well-being are real-time. Real-time software makes systems responsive, reliable, and safe, especially in cases where timing is important — from healthcare and defense to entertainment and transportation. Such applications are helpful as they process and respond to data almost instantly or within a guaranteed time frame, which is critical when timing and accuracy directly affect performance, safety, or even user experience.

As a protocol that enables real-time, two-way (full-duplex) communication between a client and a server over a single, long-lived TCP connection, WebSockets are among the technologies used by such applications.

For a long time, I thought I had password hashing figured out.

Like many Java developers, I relied on bcrypt, mostly because it’s the default choice in Spring Security. It was easy to use, widely recommended, and treated in tutorials as "the secure option." I plugged it in, shipped features, and moved on.