Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has revolutionized how organizations deploy, scale, and manage containerized applications, offering unprecedented efficiency and flexibility. However, the very characteristics that make Kubernetes so powerful—its dynamic, distributed, and ephemeral nature—also create significant challenges for monitoring. Without robust monitoring capabilities, organizations struggle to identify and resolve performance bottlenecks, optimize resource utilization, and maintain security.

One of the most compelling features of InfluxDB 3 is its built-in Python Processing Engine, a versatile component that adds powerful, real-time processing capabilities to both InfluxDB 3 Core and Enterprise. For those familiar with Kapacitor in InfluxDB 1.x or Flux Tasks in 2.x, the Processing Engine represents a more streamlined, integrated, and scalable approach to acting on data.

Are you tired of always digging through data and not finding what you're looking for? We get it. Troubleshooting and data analysis should be easier, not harder, especially when time is of the essence. To simplify your work life, we’ve introduced several powerful new features designed to eliminate wasted time and help you focus on what matters: less time troubleshooting and more time building.

Web applications generate a wealth of performance data, but it’s challenging to know exactly which metrics are the most useful for monitoring your user experience. Focusing on irrelevant metrics wastes time and resources—but if you pare down the data you’re observing too much, you may miss critical insights.

Java remains one of the most widely used programming languages today, especially in enterprise backend systems—and for many good reasons. With each new release, Java’s robust runtime offers additional improvements in performance, security, scalability, and developer productivity. The portability of its code has proven increasingly relevant and useful as the industry embraces ARM64, making Java one of the go-to languages for modern workloads.

Consistent with the words of Darwin, there is a "survival of the fittest" mentality in the DevOps and container orchestration ecosystem. Back in 2019, when Cycle came to market, Kubernetes was among the popular choices for managing containers and remains one of the most prominent players in the space today.

Achieving the ideal inventory turnover ratio is essential for businesses striving for profitability and operational efficiency. This key metric, which measures how often inventory is sold and replaced within a given period, reflects your ability to balance inventory levels, meet demand, and optimize resources. Let’s explore what defines a good ratio, how to calculate it, and strategies to improve it.

The Calico eBPF data plane is your choice if latency is your primary concern. It was very disturbing that some benchmarking brought to our attention that eBPF had higher tail latency than iptables. The 99+% percentiles were higher by as much as a few hundred milliseconds. We did a whole bunch of experiments and we could not crack the nut until we observed that there are some occasional and unexpected TCP reset (RST) packets, but no connections were reset.

Apache Kafka is the backbone of modern data streaming architectures, enabling real-time data movement, stream processing, and event-driven applications at scale. It enables high-throughput messaging between data sources and analytics platforms, supports log aggregation, and facilitates scalable extract, transform, load (ETL) pipelines for continuous data transformation and storage.