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.

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.

There are two types of alerts you don't want: We call these false alerts. As a person with responsibility over your IT infrastructure, it is natural that you have configured your monitoring systems to alert you at every step. But when these false alerts take up too much of your time, one of these unfortunate scenarios may occur: Let's explore more about false alerts before we dive into five strategies to avoid them.

Enabling accurate exchange of information between key applications has become crucial in today’s hybrid and complex IT operations. When we speak with potential customers, one common question we hear is, “How easy is it to consume and integrate the insights generated by Network Observability by Broadcom?” This might sound like table stakes, but it is often a challenge due to siloed teams, the high levels of expertise required, different data formats, and time-consuming processes.

Absinthe is a popular GraphQL toolkit for building robust APIs in Elixir. Monitoring such APIs is essential to ensure performance, detect bottlenecks, and handle errors effectively. AppSignal offers a seamless way to monitor and gain insights into your Absinthe-powered GraphQL APIs, enabling you to keep applications performant and reliable.

Debugging a Python app can be frustrating, especially when an unexpected crash leaves behind nothing but a vague error message. A well-configured exception log can make all the difference, turning guesswork into clear insights. Here’s how to set up logging that actually helps.