Data complexity increases every year. The three Vs of data—volume (the amount of data streaming in and out), velocity (the speed of generation, processing, and streaming), and variety (different forms ranging from structured databases and semi-structured XMLs to completely unstructured data as media files)—are also increasing in complexity.

To keep up with competition, enterprises must embrace next-level capabilities like artificial intelligence, machine learning, and the Internet of Things (IoT). Market leaders know this depends on having fast, resilient, reliable, and secure connectivity that can adapt to the business's needs. Organizations have to ensure their: This is where Site24x7's network monitoring tool comes in. It offers actionable insights and advanced features to keep your network running smoothly.

APM is foundational in assessing an application's internal health. It employs a variety of tools and techniques to monitor crucial metrics such as response times, error rates, and resource utilization. This comprehensive analysis enables teams to identify bottlenecks, slow database queries, and other potential performance-related issues that could diminish the user experience.

Just like how the filling makes a sandwich, an application's performance is closely tied to how effectively its middle-tier components function. While the front-end is what users see and interact with (UI), and the back-end deals with data storage, the middle tier forms the vital core where the real magic happens—processing, logic implementation, and enforcement of business rules.

Kubernetes is known to be the best container orchestration tool, but it can also add complexity to resource management, particularly as your clusters expand. Without proper monitoring, problems can rapidly worsen, resulting in subpar application performance, service interruptions, and higher expenses. In this blog, you will learn the key metrics for monitoring Kubernetes performance and how monitoring these can assist you in maintaining optimal performance in your environment.

Instead of sifting through mountains of metrics across different network types, what if you had an AI-powered tool that could display all the necessary insights in an easy-to-understand format?

Microservices are a way of designing applications as a set of small, independent services. Each service handles a specific task and interacts with others through APIs. This architecture makes it easier to develop, deploy, and scale services individually, offering greater flexibility compared to traditional monolithic systems.

Servers are restarted mainly due to two reasons.

Now that AI has revolutionized IT forever, what are its implication on IT observability? Typically, IT operations, SREs, and DevOps professionals use IT observability to gain a holistic view of their IT infrastructure. In that pursuit, they used AIOps in several ways. Now, AI has helped IT observability with better anomaly detection, faster root cause analysis, and proactively identifying opportunities to dynamically scale IT to ensure uptime, performance, and security.

Kubernetes events play a predominant role in helping ensure the peak performance of your Kubernetes clusters. These occurrences reflect important changes in states and offer immediate insights into the activities within your clusters. Whether a pod fails to initialize, a node becomes unreachable, or an application deployment encounters problems, Kubernetes events help you comprehend the root causes of these occurrences.