The Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics) offers the freedom and flexibility for monitoring application performance. But we’ve also heard from many of our users and customers that you need a solution that makes it easier and faster to get started with application monitoring.

With this guide, empower your SRE team to achieve enhanced alert remediation and incident management.

As a Site Reliability Engineer (SRE) or DevOps professional, you are well aware of the importance of observability in ensuring the smooth functioning and performance of your applications. Observing and monitoring your applications can help you identify and resolve issues in real-time, resulting in increased reliability and improved user experience. Logs play a crucial role in this process as they provide detailed information about the activity and behavior of your applications.

Fintech, an abbreviation for financial technology, encompasses many firms and technologies that employ innovation and tech to enhance and automate financial services and operations. Their goal is to enhance the efficiency, accessibility, and user-friendliness of financial services. Fintech entities span numerous sectors within the financial industry, such as online payments, lending, digital banking, investing, insurance, and more, all aimed at streamlining financial processes.

Stop me if you’ve heard this one before: you just pushed and deployed your latest change to production, and it’s rolling out to your Kubernetes cluster. You sip your coffee as you wrap up some documentation when a ping in the ops channel catches your eye—a sales engineer is complaining that the demo environment is slow. Probably nothing to worry about, not like your changes had anything to do with that… but, minutes later, more alerts start to fire off.

If you’re juggling complex, cloud-based, containerized systems and aiming to meet high customer expectations, your old monitoring processes probably don’t cut it anymore. Increasing infrastructure complexity means you need to instrument more, log more, and monitor more. That leads to even more complexity. The answer is better observability, right? Yes and no. Observability and monitoring are critical, but they are only part of what you need for service awareness and availability.

In telemetry jargon, a pipeline is a directed acyclic graph (DAG) of nodes that carry emitted signals from an application to a backend. In an OpenTelemetry Collector, a pipeline is a set of receivers that collect signals, runs them through processors, and then emits them through configured exporters. This blog post hopes to simplify both types of pipelines by using an OpenTelemetry extension called the Headers Setter.

Observability first emerged as a focal point of interest in the DevOps community in the 2017 time frame. Aware that business was demanding highly adaptable digital environments, DevOps professionals realised that high adaptability required a new approach to IT architecture. Whereas historically, digital stacks were monolithic or, at best, coarsely grained, the new stacks would have to be highly modular, dynamic, ephemeral at the component level, and spread over multiple cloud-based services.

What do the gauges on your car, the jumbotron at a stadium, and Splunk Observability Cloud all have in common? They all show you the important information to know how something is performing. We all know what to look for in the first two examples.

Elastic Observability customers saw 243% ROI and $1.2 million in savings over 3 years For government and education organizations around the world, facilitating an efficient, reliable customer experience is essential when providing critical services and building trust with stakeholders. As technology infrastructure expands and the IT landscape becomes a complex mix of private cloud, public cloud, and air-gapped environments, the ability to see across all systems and data is challenging yet critical.