Operations | Monitoring | ITSM | DevOps | Cloud

We recently explored what the cloud is and how it has evolved over 60 years. Yet, cloud computingWe recently explored the cloud and how it has evolved over 60 years. Yet, cloud computing emerged into what it is today in 2002, when Amazon, the e-commerce giant, sought a more efficient way to expand its online marketplace. So, it is unsurprising that the company’s cloud services subsidiary (AWS), which launched in 2006, is today’s largest Cloud Service Provider (CSP).

In today's fast-paced digital landscape, ensuring the reliability and performance of your applications is paramount. Heroku, a cloud-based Platform-as-a-Service (PaaS), simplifies application deployment and scaling. However, to fully leverage Heroku's capabilities, effective monitoring is essential. This guide delves into best practices for monitoring Heroku applications, providing context, practical steps, and unique insights to enhance your observability strategy.

In this guide, we'll harness AppSignal to detect, diagnose, and remove performance bottlenecks and employ proper tracing in a Ruby on Rails application. From setting up tracing to capturing errors and logging, we’ve got you covered. We'll ensure our application runs smoother than ever, even under the heaviest loads! But first, let's quickly touch on how to define tracing and its benefits.

Fluentd is popular for its flexibility and extensive plugin support, making it easy to collect, process, and forward logs from many different sources. However, as environments scale and observability needs evolve, teams often seek alternatives that offer lower resource usage, easier configuration, broader telemetry support, or tighter integration with their existing toolchains.

We’re excited to announce that Early Warning Signals are now available in Slack! Early Warning Signals help you detect service disruptions before they’re officially reported. Now, these critical notifications will show up directly in your Slack workspace, keeping your team in the loop without having to check your email.

We've all been there. Users report that applications are slow, calls are dropping, or that "the internet is broken." Yet, a glance at the network dashboards shows a sea of green—latency looks acceptable, packet loss is minimal, and bandwidth seems fine. This common scenario highlights a fundamental challenge in network observability: the perceived disconnect between the technical measurements we gather and the actual experience of the people using our digital services.

Elastic collaborates with Red Hat on the validated pattern to enhance financial analyst workflows with RAG-powered search. We’re excited to share that Elastic and Red Hat have partnered to create validated patterns that integrate Elasticsearch’s generative AI (GenAI) and vector search capabilities with Red Hat OpenShift AI. This integration can run on accelerated hardware on-prem or in IBM Cloud to power retrieval augmented generation (RAG) solutions.

Let’s be honest. AI features can sound cool on paper, but too many tools overpromise and underdeliver. At Logz.io, we didn’t want to build “yet another AI chatbot.” We wanted to create something our engineers and yours would actually use when incidents hit, logs explode, or someone asking, “What just happened to production?” Here’s how our AI Agent evolved from a basic chat interface to an incident-resolving, log-analyzing, doc-digging, context-aware assistant.

Imagine this. You’re walking your dog, sipping a coffee, when your phone buzzes: Each ping comes from a different app, but they all have one thing in common: notification software. It’s woven into our everyday lives – quietly buzzing, blinking, or nudging us with information. But when it comes to critical operations in IT, healthcare, logistics, or utilities, not all notifications are created equal. Some are simply updates. Some are mass notifications. Others require immediate action.