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. 

The most common mistake that we could usually see is increasing heap size without understanding allocation patterns. A bigger heap often delays a problem rather than solving it.

An Excel file can also be used to perform data-driven API testing. It allows testers to store multiple test data in one place, where we can easily add, update, or remove test cases without changing the automation code. It allows non-technical members, such as Business Analysts and Product owners, to understand and edit the test data to perform robust testing.

This article distills the frameworks, patterns, and hard lessons from those engagements into a practical guide for teams facing similar challenges.

Resource Allocation in Kubernetes

In this section, we’ll step outside the JVM and look at the infrastructure layer. Understanding how Kubernetes allocates resources, and how your Java application perceives those allocations, is fundamental to ensuring that you allocate the right amount of resources to your JVM.

Detailed Java code examples illustrate each pattern from a blocking wrapper with a thread pool and queue to a non-blocking implementation using CompletableFuture and virtual threads to a Reactor-based example. We also show a gRPC client/server stub, a batching implementation, Resilience4j integration, and Micrometer/OpenTelemetry instrumentation, as well as performance considerations and deployment best practices. Finally, we offer a benchmarking strategy and a migration checklist with anti-patterns to avoid.