1. Event Production with Spring Boot and Kafka
2. AI-Driven Processing in Kafka Consumers
3. Real-Time WebSocket Delivery to the Frontend

Event Production with Spring Boot and Kafka

The first step is capturing an event and publishing it to Kafka. By offloading work to Kafka the application can respond immediately to the user without waiting for processing. Spring Boot’s integration with Apache Kafka provides a KafkaTemplate to send messages to topics.

Just like POST requests, the content-type header is important because it tells the server how to interpret the data we’re sending.

This article covers the four frameworks I have personally used to ship Java AI applications: Genkit Java, Spring AI, LangChain4j, and Google ADK Java. Each one represents a meaningfully different bet on what a Java AI framework should be, and understanding those differences will save you from picking the wrong tool.

Most of our daily work happens safely inside the JVM. Memory management, threads, and file handling — the JVM abstracts these away nicely.

In this article, I will share how we adapted our Java backend for container orchestration. I will explain the specific lifecycle issues we encountered. I will detail the configuration changes that solved the dropout problem. This is not a guide on writing Dockerfiles. It is a record of the operational friction we faced when Java met Kubernetes. Building cloud-native Java apps requires more than just packaging a JAR. It requires understanding how the orchestration layer interacts with the JVM.

In my recent work, migrating a monolithic Java application to a microservices architecture, we faced this exact triad of issues. We were treating Docker containers like lightweight virtual machines and ignoring the nuances of how the JVM interacts with container boundaries. The result was bloated infrastructure costs, slow CI/CD pipelines, and unstable production pods.

Most Java applications that integrate with AI models do something like this:

String userInput = request.getParameter("topic");
String prompt = "Summarize the following topic for a financial analyst: " + userInput;

In our recent migration to a cloud-native architecture, the culprit was a hidden memory configuration issue involving how the Java Virtual Machine interacts with Kubernetes container limits. A tiny mismatch in resource allocation, something that went unnoticed during development, led to a chain reaction of OOMKilled events in production.

In this article, I will share how we migrated from a tightly coupled API architecture to an event-driven design using Java and Kafka. I will explain the specific challenges we faced during the transition. I will detail the code changes required to handle asynchronous communication. This is not a theoretical discussion about microservices. It is a record of the practical steps we took to stabilize our platform. Building resilient backend systems requires more than just choosing the right tools. It requires understanding the trade-offs between consistency and availability.

And that difference shows up later — in architectural decisions, debugging complexity, vendor lock-in, and, ultimately, career growth.

Since I am comfortable with using Java, I will use it to define a neural network (NN) model, train it, export it in a language-agnostic format, and then import it into a Spring Boot project. Now, doing all this from scratch would not be advisable, since there are many advances in the field of NN that would take a long time to properly understand and implementing them would be difficult and error-prone. So, to both learn about NNs and make implementation easy, we will use a proven software platform: TensorFlow.

In the latest Java versions, the concurrency model has evolved significantly. Modern Java introduces improvements such as Virtual Threads, better executors, improved fork-join performance and more structured concurrency approaches. These features allow developers to build highly concurrent applications with simpler code and fewer scalability limitations.

In this article, I will share how we diagnosed the bottleneck. I will explain the profiling tools we used. I will detail the code changes that restored performance. This is not a theoretical guide. It is a record of a real production incident and the steps we took to resolve it.

As one practitioner notes, Jenkins is a powerful CI/CD tool that significantly reduces manual effort and enables faster, more reliable deployments. By treating the entire delivery pipeline as code, teams get repeatable, versioned workflows that kick off on every Git commit via webhooks or polling.

This tutorial walks through practical approaches to efficiently test GET APIs and build reliable automated checks, including:

Apache Spark 4.0 represents a major evolutionary leap in the big data processing ecosystem. Released in 2025, this version introduces significant enhancements across SQL capabilities, Python integration, connectivity features, and overall performance. However, with great power comes great responsibility — migrating from Spark 3.x to Spark 4.0 requires careful planning due to several breaking changes that can impact your existing workloads.

This comprehensive guide walks you through everything you need to know about the Spark 3 to Spark 4 migration journey. We'll cover what breaks in your existing code, what improvements you can leverage, and what changes are mandatory for a successful transition. Whether you're a data engineer, platform architect, or data scientist, this article provides practical insights to ensure a smooth migration path.

Agentic AI — autonomous systems that reason, plan, use tools, remember context, and execute complex multi-step tasks — is moving from experimental prototypes to production workloads in enterprises. While Python ecosystems (LangChain, LlamaIndex, CrewAI) led the early wave, Java is emerging as a serious contender for mission-critical agentic applications.

Example: 

Motivation

In a large organization, the scope of dependencies review given by build-time scanning has some limitations.

Intended Audience

The audience targeted in the article is those an enterprise architect, senior Java developer, security architect, and DevSecOps teams who are required to design, modernize or secure large-scale Java applications. In recent years, there are a number of breaches of enterprises that have not been initiated by a zero-day exploit but a known vulnerability, which has not been prioritized e.g. an outdated library, an open API, or a poorly configured integration In a number of instances, the organizations were technically compliant but still exposed because of the homogenous, checklist-driven security measures that did not concentrate on the high-risk elements.