A notification system often starts with a simple requirement: send an email, text message, or push notification when something happens in an application. For a small system, calling an external provider directly may be enough. As traffic grows, however, notification delivery becomes a distributed systems problem. A production notification platform needs to handle traffic spikes, provider failures, duplicate events, retries, delivery tracking, and multiple notification channels without slowing down the application that generated the notification.
This article explores how a scalable notification system can be designed using Spring Boot and Kafka, with a focus on asynchronous processing, fault tolerance, idempotency, retries, and horizontal scaling.
Java Trends of 2026: Market Position, Enterprise Adoption, Version Distribution, and the AI Acceleration Angle
2026 Java trends: TIOBE and Stack Overflow rankings, enterprise adoption by industry, Spring Boot and framework data, Java version distribution, AI/ML integration, and runtime performance benchmarks.
Post-Quantum Cryptography Support in Java
Quantum computers are advancing, and they could soon break the encryption that protects today’s data. RSA and ECC, which are standard today, may not stand up to quantum attacks. This makes post-quantum cryptography a key part of planning for long-term security. Java is already preparing for this future. New updates in the JDK add support for quantum-safe algorithms, giving developers …
Spring AI: An Overview
Integrating AI into Java projects has traditionally been complex—requiring multiple SDKs, custom integrations, and provider-specific code. Spring AI simplifies this process by providing a single, consistent layer for working with large language models in Spring Boot. No more stitching together libraries or rewriting code for every provider. In this guide, we’ll explore what Spring AI is, why it matters for …
Writing Java Code and Unit Tests Faster with GitHub Copilot
In this post, we’ll explore how to use GitHub Copilot to generate Java code and unit tests with minimal manual input. Using a real-world example—a mortgage calculator service—you’ll see how Copilot can help write both the core logic and the corresponding unit tests. Whether you’re new to AI-assisted development or curious about Copilot’s capabilities in a Java environment, this tutorial will give you practical insight into how it works—and where human oversight still matters.



