Research published by Cisco AppDynamics highlights the challenges that IT teams are facing in managing application availability and performance within hybrid IT environments. The new report, The Age of Application Observability, reveals the levels of complexity that technologists are encountering as they implement cloud native technologies alongside existing on-premises applications and infrastructure.

OpenTelemetry is a popular open-source project that provides a standardized way of collecting, processing, and exporting telemetry data from distributed systems. It is designed to be vendor-neutral and supports multiple programming languages and platforms. OpenTelemetry consists of several components that work together to enable telemetry collection and processing.

Datadog’s modern observability empowers development engineers with full-stack visibility, comprehensive instrumentation generation, and proactive alerts to accelerate software development releases and address potential incidents. While Datadog gives teams end-to-end visibility, it works even better together with AIOps from BigPanda – development teams gain insights into outside application dependencies and reliance on other systems.

The latest versions of Elastic Observability’s most popular observability integrations now use the storage cost-efficient time series index mode for metrics by default. Kubernetes, Nginx, System, AWS, Azure, RabbitMQ, Redis, and more popular Elastic Observability integrations are time series data stream (TSDS) enabled integrations.

How AppDynamics APM, Cloud Native Application Observability and the Cisco FSO Platform can provide a full-stack, holistic view of application performance, user experience, security posture and business impact. Recently, a customer asked me how Cisco’s investment in full-stack observability benefits traditional AppDynamics customers. To answer, first, let’s take a moment to discuss how Cisco AppDynamics fits into Cisco Full-Stack Observability.

Observability gets more challenging yearly in the rapidly evolving world of distributed computing and cloud-native applications. Organizations today are tasked with ensuring that their critical business applications, revenue-generating applications, and supporting infrastructure operate with reliability and security. The stakes are high; any lapse can lead to user churn, revenue loss, and decreased productivity.

OpenMetrics and OpenTelemetry are two popular frameworks that provide metrics-driven insight into complex software environments. Understanding the differences between these two options is critical for organizations looking to optimize their monitoring capabilities. In this article, we will explore the key features, benefits, and considerations of OpenMetrics and OpenTelemetry to help you make an informed decision when choosing the right solution for your organization.

In Part 1 of this series, you learned the core components of Kubernetes, an open-source container orchestrator for deploying and scaling applications in distributed environments. You also saw how to deploy a simple application to your cluster, then change its replica count to scale it up or down. In this article, you’ll get a deeper look at the networking and monitoring features available with Kubernetes.

OpenTelemetry and Prometheus are both powerful observability tools, each with its own strengths. OpenTelemetry provides a standardized way to collect, instrument, and export telemetry data, while Prometheus excels at time-series monitoring and alerting. In this blog post, we will delve into the similarities and differences between OpenTelemetry and Prometheus, exploring the benefits and advantages of each.

According to the Uptime Institute’s 2022 Outage Analysis report, one out of every five companies has experienced a “serious” or “severe” incident over the past three years—a percentage that’s increasing. Those incidents are expensive: over 60% cost more than $100,000, while 15% set their companies back close to $1 million.