This simplicity is especially valuable in distributed and cloud-native systems, where latency, throughput, plus scalability directly shape user experience and infrastructure costs. A key-value database offers a focused approach: identify data by a key and retrieve or update it efficiently. The challenge is selecting a technology that delivers this performance while meeting the operational maturity, ecosystem support, and governance standards required for enterprise applications.

Data Streaming Meets the Lakehouse

Data lakes promised to solve the enterprise data problem. The reality has been messier. Batch pipelines produce stale information, and analytical workloads run hours after the business event occurred. By the time a query runs, the window for action is often already closed.

In Java-based API automation, REST Assured combined with Hamcrest Matchers provides a clean and expressive way to verify API responses. These matchers help testers write readable assertions that validate numbers, strings, arrays, JSON objects, and collections with minimal code.

Disclaimer: The views and opinions expressed in this blog are strictly my own and do not necessarily reflect the official policy or position of my employer.

Most probably, it will be surely mentioned that there’s a Lombok annotation called exactly @SneakyThrows that solves the problem immediately when put on a method. Last but not least, to outline it in a more pragmatic manner, the concept allows tricking the Java compiler into treating checked exceptions as runtime exceptions.

Java applications interact with Excel more often than most teams plan for: file uploads from finance, calculation logic authored in a workbook, reporting exports back to business users. The files these users produce today are not the same as the files they produced five years ago. Excel 365 and Excel 2021 introduced a new formula model, and workbooks authored in those versions routinely use it. Depending on which library you use, those formulas may evaluate correctly, fail silently with stale cached values, or throw exceptions at recalculation time.

Now: who reviews it?

I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work

This article discusses how modern languages help developers handle behind-the-scenes file descriptor, kernel memory, and heap management. These three concepts are major bottlenecks for scaling.

Donald Ervin Knuth 

Can we do the same thing in Neo4j?

Autonomous AI coding agents like Goose go far beyond simple code autocompletion. Built in Rust for speed and portability, Goose runs on your local machine, inspects files, runs terminal commands, and uses tools over MCP to automate complex engineering tasks.

Apache POI has been the standard Java library for reading and writing Excel files for over twenty years. It handles the majority of everyday spreadsheet tasks well. But a growing category of real-world Excel files now contains formulas that POI's evaluator cannot execute at all.

This is one of several situations Java developers hit when working with spreadsheets that are not obvious until you are already in production. Business users produce, share, and reason about data in spreadsheets. Finance teams model in Excel. Operations teams track inventory in Excel. Analysts hand deliverables to engineering as .xlsx files. Java applications end up interacting with all of it: back-office services accept Excel uploads, pricing engines run calculations that were originally authored in a workbook, reporting tools export data in a format the recipient can open in Excel without formatting problems.

Event-driven architecture is widely used in enterprise systems to decouple services, absorb traffic spikes, and move work out of request paths. Java Message Service (JMS), now standardized as Jakarta Messaging, remains common in systems built around ActiveMQ, IBM MQ, Solace, TIBCO EMS, and similar brokers.

Java 21 virtual threads give these systems another scaling option. A JMS listener often spends more time waiting on a database, HTTP service, cache, or file system than it spends using the CPU. Moving that blocking work to virtual threads can reduce platform-thread pressure without forcing the application into a reactive programming model.

The short answer is yes. You do not need to abandon Java or wait for a new platform to build AI-enabled applications. Java already provides a mature ecosystem of AI libraries, model providers, APIs, and integration patterns. Jakarta EE offers the capabilities required to deploy these technologies in production-grade enterprise systems today.

Our PyFlink pipeline was missing its latency SLO by seconds. The pipeline itself was straightforward: consume events from Kafka, transform them, serialize them as Protobuf, and write the results to downstream systems. Yet under production load, p99 end-to-end latency was consistently in the 3-5 second range.

Profiling pointed us to an unexpected bottleneck: we were deserializing Protobuf messages in Python, even though the Flink runtime processing our stream was JVM-based. Every record that entered the Python path had to cross the JVM-to-Python process boundary, get parsed by a Python UDF, and then cross back. The business logic wasn't the problem. The doorway was.

Historically, Java developers faced a tough choice: either rely on heavyweight, external workflow clusters (like Temporal or Camunda) that add operational overhead, or hand-craft fragile while loops and custom state machines inside their services.

"As a result, some HTTP servers allow it, and some—especially caching proxies—don't. [...] However, because GET with a request body is not universally supported, the search API also accepts POST requests."

]]> Thu, 06 Aug 2026 17:00:00 GMT https://dzone.com/articles/3667011 Hüseyin Akdoğan https://dzone.com/articles/java-version-manager Every Java developer knows the ritual. A JAVA_HOME export in one profile file, a different one in another. sdk use java 21 in this terminal, but the other terminal is still on 8. The build passes in your shell and fails in the IDE because the IDE launched from the dock and never sourced your init line. A teammate's "works on my machine" that turns out to mean "works on my shell."

I got tired of it, so I built Jolta. It's Volta, but for Java. This article is partly about what it does, but mostly about how I built it, because the process is the part I'd recommend to anyone building in a crowded tool category: I mined my competitors' bug trackers and turned their backlogs into my test suite.

]]> Tue, 04 Aug 2026 17:00:05 GMT https://dzone.com/articles/3666526 David Lerner https://dzone.com/articles/rethinking-java-design-patterns The functional programming answer, to those who wonder how to integrate or combine it with object-oriented programming, is usually: Turtles all the way down.

This is an aphorism whose origin is credited to Richard Feynman. In his book, Surely You're Joking, Mr. Feynman !, published in 1985, he tells the story of one of his conferences on the nature of the universe, where he was challenged by someone in the audience, saying that the universe rests on a turtle. Feynman asked then what the turtle is resting on, and the answer was: "another bigger turtle".

]]> Tue, 04 Aug 2026 12:00:07 GMT https://dzone.com/articles/3666595 Nicolas Duminil