From detection to resolution: The DEM workflow

DEM is a proactive, data-driven approach that goes beyond problem identification to understand and enhance the entire customer journey. Crucial is the workflow, a laborious process that begins with detection and concludes with resolution.

The first step is to listen to your customers—and their digital interactions. A strong DEM to...


DEM 101: Understanding and implementing digital experience monitoring

Modern businesses need a fast, reliable, and seamless digital experience. Proactive monitoring of the user experience—understanding how users interact with all digital touchpoints—is vital. This blog post explores the fundamentals of this approach, its significance, and key implementation strategies.

DEM is a way to track th...


The critical role of Kafka monitoring in managing big data streams

However, ensuring that your Kafka infrastructure operates smoothly is not a task you can simply set and forget. Due to the large volume of incoming data, issues like system slowdowns, bottlenecks, and unexpected breakdowns can happen at any time. This is why monitoring Kafka is essential. By closely observing system health, performance, and d...


5 strategies to reduce false alerts in server monitoring

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 occu...


The importance of benchmarking in digital experience monitoring

Making sure that users have a smooth, pleasurable experience with your digital platform—whether it be a website, mobile application, or any other online service—is essential for business success. Benchmarking is a crucial technique that can increase the usefulness of digital experience monitoring. By offering a point of comparison...


Why traditional event correlation falls short in modern IT and how AIOps can help

Eodern IT involves an expanding use of AI, enhancements from the DevOps culture, and traditional uses of containers, virtual machines, microservices-led architecture, multiple cloud, and others. Monitoring technology has not entirely caught up with contemporary IT needs due to various reasons. Traditional monitoring methods were often patched...


The ultimate guide to cloud-native application performance monitoring with AWS, GCP, and Azure

The rapid adoption of cloud-native applications has revolutionized how businesses innovate, scale, and optimize costs. These applications leverage microservices, containers, and serverless functions, allowing seamless collaboration across multiple platforms like AWS, GCP, and Azure. However, managing performance in such a distributed environm...


Troubleshooting Kubernetes deployment failures

When something goes wrong during application deployment, it becomes all the more crucial to diagnose the issue methodically and get things back on track. This guide walks you through practical steps for troubleshooting deployment failures efficiently.

A Kubernetes deployment is a vital component for managing and automating the rollout p...


Monitoring for Kubernetes API server performance lags

The Kubernetes API server is a key component in the control plane. Every interaction, whether deploying applications, scaling workloads, or monitoring system health, depends on the API server. Consider the human body: We have the brain as the critical organ, and the nerves function as the control system. The Kubernetes API server is like...


Handling persistent storage problems in Kubernetes clusters

Persistent storage is the backbone of stateful applications running in Kubernetes. Whether you are managing databases, logs, or application states, ensuring transactional data remains intact despite pod restarts or node failures is a challenge. In this blog, we will discuss the most common persistent storage issues in Kubernetes and how to ha...