Java Remains Relevant in Modern Software Development

Kafka Streams is too complicated for the average Java developer. There, I said it. When teaching Kafka I notice a pattern over and over again: Developers would rather build a brittle app with low-level Consumers and Producers than learn the "magic" of Kafka Streams. The reason: understanding Consumers and Producers is easy. It seems to just work without needing a deep dive into Kafka internals. The problem is that many developers then cobble Consumers and Producers together into a simple app that consumes messages, transforms them, and produces them. They do this without thinking about reliability guarantees at all. Have you tried to implement transactional semantics yourself with only the low-level clients? Good luck with that. My recommendation? Use Kafka Streams. But Kafka Streams is a different beast. It requires a shift to functional thinking. For a developer who has spent years perfecting imperative, “normal” Java, being forced into maps and aggregations feels like a totally different world where your hard-earned experience doesn't seem to apply. I don’t see a simple solution. Kafka Streams works very well for experts, but we need to rethink the API for the rest of the world. Consumers and Producers are too low-level. Kafka Streams is too complicated. I would love to see a unified client library that makes it simple to use Producers and Consumers safely. A library that handles transactions and exactly-once semantics in a way that works for most cases. I really love how the Faust Streaming API handles this simplicity and reliability. But that is a topic for another article. If you are into stream processing, have a look.

I somehow disagree with this post, though I understand where it's coming from. I believe Kafka Streams is a powerful accelerator for the average Java developer. That said, the DSL is a trap (eg. Joins). It abstracts away too many things and forces a typical developer to shift their mindset too much. It was designed at a time when KSQL was the target. If you focus on understanding only the .process stateful operator (on top of stream, map, filter, and other stateless ones) along with some basic state store monitoring, you can achieve ANYTHING with Kafka Streams. As soon as you understand the basic .process operation, you get a straightforward stateful microservice framework where you can reuse all your Java knowledge :) I've had the opportunity to train 20+ organizations on Kafka Streams, and it usually takes only 2 to 3 days: just a matter of explaining the right simple concepts in the right order. I'll present a live design session on Kafka Streams at Current London 2026, happy to exchange on this Anatoly Zelenin 😉

The good guy Anatoly is a KafkaStreams teacher, and tell a (well known) story about many Java developers see KafkaStreams as too much complicated. But KafkaStreams is complicated only for people who don't want update themselves, who don't recognise that sometimes a new mindset is required in computer science. Really, KafkaStreams is not so complicated; instead it resolves a lot of situations you could fall into when using simple consumer-producer: fault-tolerance, exactly-once, automatic retry, and of course complex using of multiple topics together (join, merge, etc etc). About fault-tolerance: imagine if you have to implement manually the fault-tolerance, storing the state in external place (db, fast-db, ...) and then retrieve when the app restart; this implies you ensure the snapshot is saved-loaded in a very safe way, to not lose data; also, imagine if you would have all this in a multi-threading and also multi-instance (k8s, okd, ...) environment. No, you wouldn't implement this, trust me. Instead, trust in KafkaStreams.

For years, we’ve accepted that running Java applications meant running on the JVM. But what if that assumption no longer holds? I just published a new article exploring how Spring Boot applications can be compiled into native executables using GraalVM Native Image—dramatically reducing startup time and memory usage, which is especially relevant in cloud and serverless environments. Native images are built ahead-of-time into standalone binaries, eliminating the need for a JVM at runtime and enabling faster startup and lower resource consumption. This is not just about performance—it’s about rethinking how we build and run Java in a world where every millisecond and megabyte has a cost. In the article, I walk through: - The real problem behind Java resource consumption in the cloud - The trade-offs between JVM (JIT) and AOT compilation - How Spring Boot AOT makes native compilation viable - A minimal working example using Gradle and modern Java If you’re building microservices, running in containers, or exploring serverless with Java, this might change how you think about your architecture. 👉 Read the full article: https://lnkd.in/dcAvBVTz Curious to hear your thoughts—are native images something you’re already using or still evaluating?

This is good news, but at the same time gives me the feeling that Spring Boot is playing catch up with Quarkus. Developers are the winners in all this, because they get to finally enjoy native images in their Spring Boot projects.

🚨 5 Kafka design patterns every Java backend engineer should know A consumer group was 3 hours behind. The system looked fine. No alerts in the ops channel. But the metrics were already telling a different story. That’s the danger of Kafka in production: it often fails silently first. After building high-throughput systems in Java, one lesson became very clear: Kafka is not a database. Kafka is not just a fast queue. These are 5 patterns that consistently make Kafka systems more reliable at scale: 1️⃣ Outbox Pattern When the database is updated but the event is not published, your system becomes inconsistent. The Outbox Pattern solves this without relying on distributed transactions. 2️⃣ Retry Topics + DLQ Retrying endlessly in the same flow can block partitions and increase lag. Retry topics + DLQ create a safer recovery path and isolate failure properly. 3️⃣ Log Compaction for State Kafka is not only for event streaming. With log compaction, it can also act as a distributed state backbone for specific use cases. 4️⃣ Controlled Concurrency More threads do not always mean more throughput. In Java consumers, concurrency must be controlled carefully while preserving ordering where it matters. 5️⃣ Saga Pattern Distributed systems do not really “rollback.” They compensate. That is why Saga is essential when workflows span multiple services. 💸 Bonus: cost matters too. Poor retry strategies, duplicated events, and cross-zone traffic can make Kafka far more expensive than expected. Even simple optimizations like ZSTD compression can create meaningful savings. 🎯 Final thought Kafka usually looks easy in the beginning. The complexity only appears under scale, failure, and pressure. That’s why patterns matter. What silent Kafka issue have you seen in production?

If you are serious about becoming a solid Java developer, mastering the right tools matters just as much as mastering the language itself. Here are 9 Java libraries and frameworks that consistently show up in real world projects: • #Apache Commons Lang for everyday utility functions • #Google Guava for collections, caching, and clean abstractions • #Jackson for fast and reliable JSON processing • #SLF4J and Logback for structured and flexible logging • #Hibernate for simplifying database interactions with ORM • #SpringFramework for building scalable enterprise applications • #SpringBoot for rapid development with minimal configuration - built on top of Spring framework • #JUnit for writing reliable and maintainable tests • #Maven or #Gradle for dependency management and build automation Knowing these is not about memorizing tools. It is about understanding how to build clean, scalable, and production ready systems. If you are teaching or learning Java, this is a strong foundation to focus on. Which of these do you use the most in your projects?

will c# and go will replace java springboot No, C# and Go are unlikely to replace Java Spring Boot. Instead, they coexist as specialized alternatives, each dominating different niches of modern software development. Market Dynamics & Positioning Java Spring Boot (The Enterprise Standard): It remains the "gold standard" for large-scale enterprise systems, fintech, and complex banking applications due to its massive ecosystem, stability, and extensive library support. C# / .NET (The Microsoft Powerhouse): C# is the primary choice for organizations heavily invested in the Microsoft ecosystem and Azure. While it offers similar capabilities to Spring Boot, it is generally seen as a competitor rather than a replacement. Go (The Cloud-Native Specialist): Go is increasingly preferred for microservices, high-performance networking, and infrastructure tools (like Docker and Kubernetes) because of its fast startup times, low memory footprint, and simple concurrency. Why Spring Boot Persists Massive Legacy Base: Millions of existing enterprise applications are built on Spring Boot. Rewriting these in C# or Go is often too costly and risky for businesses. Modern Evolution: Java is not "standing still." Features like Virtual Threads (Project Loom) and GraalVM for native compilation address the performance and memory gaps once held by Go. Talent Pool: The global availability of Java developers ensures that companies can easily hire and scale teams, which is a critical factor for long-term project viability. AI Integration: New frameworks like Spring AI allow Java developers to integrate modern AI features without switching languages.

Java developers — here's the most complete free roadmap I've seen for going from OOP basics to production-grade microservices. 🚀 30 structured sessions. 120 exercises. 100+ HackerRank challenges. Every topic covered: 𝗖𝗼𝗿𝗲 𝗝𝗮𝘃𝗮 (S1–S4) → OOP, Java 8 Streams, Virtual Threads (Java 21), Maven/Gradle 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 (S5–S8) → REST APIs, IoC/DI, JDBC, structured logging, AOP 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 (S9–S12) → JPA, Hibernate, N+1 problem, JUnit 5, Mockito, TDD 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (S13–S16) → Redis caching, Spring Security, JWT, OAuth 2.0, GitHub Login 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 (S17–S20) → Microservices, Kafka, Spring Cloud, E-Wallet major project 𝗥𝗼𝗮𝗱𝗺𝗮𝗽.𝘀𝗵 𝗘𝘅𝘁 (S21–S30) → Docker/CI-CD, GOF Patterns, GraphQL, gRPC, Observability, System Design at Scale The roadmap includes Topics, exercises at 3 difficulty levels, and HackerRank practice for every session. 📌 Full curriculum guide + exercises in comments 👇 What's the hardest Java topic you've tackled? Drop it below ⬇️ #JavaDeveloper #SpringBoot #Microservices #BackendDevelopment #JavaFullstack #100DaysOfCode #SoftwareEngineering

“Think Java is outdated? Think again.” In today’s tech landscape, Java is not just surviving — it’s evolving and powering some of the most critical systems around us. From building scalable cloud backends with Spring Boot to handling massive data pipelines with Apache Spark, Java continues to be a backbone for modern development. Here’s what makes Java still highly relevant: 🔹 Cloud-native development with Kubernetes 🔹 Event-driven architectures using Kafka 🔹 Robust ORM with Hibernate 🔹 Microservices and scalable systems 🔹 CI/CD pipelines with Jenkins 🔹 Mobile development via Android 🔹 AI-powered applications with Spring AI 🔹 Reliable testing with JUnit Java isn’t old — it’s mature, stable, and continuously adapting to new technologies. If you’re a developer, mastering Java along with its ecosystem means you’re ready to build real-world, production-grade systems. What’s your take — is Java still a top choice in 2026? 🚀 #Java #SpringBoot #Microservices #CloudComputing #BackendDevelopment #Kafka #Kubernetes #DevOps #AI #SoftwareEngineering