They spend hours tuning hyperparameters, testing new architectures, and following the latest research, only to see performance stall at the same frustrating accuracy ceiling. More training rarely fixes it. More augmentation often does not either. Even swapping one strong architecture for another may not change much.

This article is a practitioner-style comparison of the dimensions that matter most in day-to-day platform work: write-heavy operations, multi-engine reads, schema evolution, compaction, and time travel.

Querying usually starts small, we filter on a field, maybe two. The ORM makes it easy, and there’s no real reason to think about it. It does what we need.

In this article, I will share how we integrated SonarQube into our Java development workflow. I will explain the specific rules that exposed our vulnerabilities. I will detail how we configured quality gates to prevent future regressions. This is not a generic installation guide. It is a record of how we shifted security left in our pipeline. Static analysis is not just about finding bugs. It is about building a culture of quality.

You're building a SaaS application that needs to support multiple databases. Or maybe you're migrating from MySQL to PostgreSQL. Or you have different clients using different database engines.

Whatever the reason, you've likely encountered this nightmare:

The term “semantic” comes from the Greek semantikos, meaning “significant” or “meaningful,” which is central to Tactical DDD. The objective is not just to reorganize classes, but to ensure code communicates intent clearly to both engineers and business experts. In modern Java systems, where complexity increases due to distributed architectures, integrations, and ongoing business changes, this clarity is essential for long-term maintainability. 

Modern health data analytics increasingly leverage AI agent software components that process information and make decisions, often using large language models (LLMs) or machine learning models. In Java, you can build agentic systems using libraries like DJL (Deep Java Library), Spring AI, or by integrating LLM APIs. This document includes Maven setup, minimal Spring Boot code (controllers and services), a simple agent example, diagrams, and a comparison of different agent approaches.

Flowchart

"Do the simplest thing that could possibly work."

— Kent Beck, creator of Extreme Programming and pioneer of Test-Driven Development.

AWS has increasingly nudged Lambda Node.js workloads toward modern asynchronous patterns, including guidance that async/await handlers are recommended and that callback-based handler signatures are only supported up to Node.js, with Node.js requiring asynchronous work to use async handlers. This constraint is a design opportunity: Once handler execution is centered on a returned value and on predictable, composable functions, cross-cutting behavior can be expressed as functional wrappers and pipelines rather than as framework-specific magic.

Have you ever needed to generate OpenAPI documentation directly from your code and, more importantly, do it in a way that fits cleanly into a CI pipeline? Swagger UI is commonly used in Spring Boot applications to visualize and test APIs from the browser. It can also expose the generated OpenAPI definition through a configurable endpoint, and that endpoint is exactly what we will use in this article.

Whether you’re already using Swift’s async/await features or just starting to explore concurrency, this guide will give you a solid understanding of the mechanisms that keep your concurrent code safe and efficient.

For Java developers, the challenge is familiar: most computer vision examples live in Python notebooks, while the systems that actually need image classification run on the JVM. Bridging that gap usually means standing up a separate Python microservice, managing REST calls, and dealing with serialization overhead. That’s a lot of ceremony for what should be a single processing step.

The Problem: "Anonymous" Queries

When your database starts lagging (slow queries), you check the processlist in MySQL only to find a vague line:

In this tutorial, we will explore DELETE API requests and learn how to handle them with Rest-Assured in Java for automated testing. The following topics will be covered:

Legacy Custom Code Pitfalls Hidden in Plain Sight

One of the biggest sources of hidden issues is custom ABAP code carried over from ECC. Even after a successful syntax adaptation, legacy code can harbor logic that silently malfunctions in S/4HANA:

In this tutorial article, we will discuss and cover the following points:

You're running a Java application in a Docker container on your M1 Mac. Everything works fine, but you notice something strange: The resident set size (RSS) keeps growing, even though your heap usage is stable. After hours of investigation, you find mysterious rwxp memory regions, each exactly 128 MB, accumulating in your process memory map.

What's causing this? Is it a memory leak? A JVM bug? Something else entirely?

Data teams write SQL transformations to shape raw data for downstream use cases. When those queries change, they can rupture dependencies or alter metrics in unintended ways.

DuckDB exists to break that cycle. It's an analytical database that runs entirely in process, requires zero setup, and can query CSV files, Parquet, and pandas DataFrames directly — often faster than tools that cost thousands of dollars a month to run. This post is for Python developers who work with data and want a sharper tool in their kit.