Apache ActiveMQ is a widely used message broker that enables seamless communication between distributed applications. However, as the volume of messages increases, performance bottlenecks can arise, leading to slow message processing, high latency, broker crashes, and out of memory (OOM) errors. One of the most critical issues affecting ActiveMQ is OOM errors, which occur when the broker exceeds its allocated heap memory. This can result in service failures, message loss, and prolonged downtime.

Middleware plays a crucial role in modern IT infrastructure by enabling seamless communication between applications, systems, and services. It facilitates data exchange, enhances interoperability, and supports various business functions by providing capabilities like messaging, transaction management, and integration services. Over time, middleware has evolved from simple message brokers to sophisticated platforms supporting APIs, cloud computing, microservices, and event-driven architectures.

Kafka is powerful. No doubt about it. But it’s also a beast when it comes to operational complexity and cost. What starts as a simple deployment quickly turns into a resource-hungry system that eats up engineering hours, compute power, and budget. Let’s consider a company that eagerly rolls out Kafka to streamline event streaming. Year one? Smooth sailing. Everything runs fine, and the team feels great. Year two? The cracks start to show.

RabbitMQ is a powerful, reliable, and widely used message broker that forms the backbone of modern microservices architectures. However, ensuring its performance and reliability requires proactive monitoring of key metrics. In this blog, we will explore the essential RabbitMQ metrics, their units, possible issues, solutions, and how tools like Atatus can simplify monitoring and troubleshooting.

Scaling Kafka isn’t just about adding nodes or increasing partition counts; it’s about creating an ecosystem that grows with your business demands. As we move into 2025, the focus is shifting from brute force scaling to more nuanced, efficient strategies. Organizations are discovering that throwing resources at Kafka bottlenecks won’t solve long-term scalability issues—instead, optimization is king.

In a Kafka setup, high availability isn’t just nice to have—it’s a lifeline. Downtime, data loss, or hiccups in message flow can make or break critical applications. Let’s be real: setting up Kafka brokers to be resilient takes some fine-tuning, but it’s absolutely worth it. Imagine dealing with failovers smoothly or knowing your data is protected even if a broker goes down—this is what configuring for resilience is all about.