Whether you sell consumer goods, ship freight, manufacture vehicles, process payments, underwrite insurance, or manage hospital claims, your business depends on the same thing: order to cash.
Orders are created, fulfilled, invoiced, and paid.

The meshIQ blog provides insights into pivotal and disruptive technologies, such as Hybrid Cloud, Messaging Middleware, AI, and more contributed by meshIQ experts and innovators.
Whether you sell consumer goods, ship freight, manufacture vehicles, process payments, underwrite insurance, or manage hospital claims, your business depends on the same thing: order to cash.
Orders are created, fulfilled, invoiced, and paid.
Apache Kafka has become the go-to platform for organizations handling high-throughput, real-time data streaming. Its ability to manage massive data volumes while ensuring reliability is second to none. However, as businesses grow and demand for data increases, scaling Kafka isn’t always a walk in the park.
In this post, we summarize the major changes in the recently officially released Apache Kafka 4.0.0 version. We will look at the most notable features compared to the previous versions and explain what these changes mean in real production environments and what improvements they can bring to your streaming infrastructure.
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.
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.
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.
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.
Kafka clusters don’t just run on autopilot—they need regular health checks to stay stable and efficient. These checks aren’t just for peace of mind; they’re essential for preventing failures, keeping message flow smooth, and avoiding operational chaos.
Let’s be honest: setting up Kafka monitoring on Kubernetes can feel like you’re trying to solve a puzzle without all the pieces in place. Between connectivity snags, configuration issues, and keeping tabs on resource usage, it’s easy to feel like you’re constantly firefighting.