Elastic Observability provides a full-stack observability solution, by supporting metrics, traces, and logs for applications and infrastructure. In a previous blog, I showed you how to monitor your AWS infrastructure running a three-tier application. Specifically we reviewed metrics ingest and analysis on Elastic Observability for EC2, VPC, ELB, and RDS.

Gain executive-level insights on Cisco AppDynamics’ for on-premises, hybrid and cloud customers from Ronak Desai, Cisco SVP & GM AppDynamics and full-stack observability.

Most SaaS products have nice, organic growth when they work well. Employees log in, they click around and make stuff, then they share links with others who do the same. After a few weeks or months, there are thousand of objects. Some are abandoned, and some are mission-critical. Different people also bring different perspectives, so they name things that are relevant to their role and position in the team, which may be confusing to others outside their realm.

Not long ago, we announced the launch of Honeycomb’s Service Map, a new feature that gives users the ability to get an overall, filterable view of their system and how everything is connected, along with some exciting new enhancements to BubbleUp. What’s the story behind these changes? They make it even easier for developers to zero-in on issues, even when they are hidden in billions of lines of code.

Baking a delicious pizza in a wood-fired oven requires a combination of skill, experience and the right tools. The same is true for achieving optimal observability in a Kubernetes environment. In this post, we'll explore some of the lessons learned from baking pizza in a wood-fired oven and apply them to the world of Kubernetes observability.

Observability, monitoring, and telemetry are crucial for maintaining the performance and reliability of modern systems. Their concepts are often used interchangeably, but they have distinct differences that are important to understand. In this blog, we’ll explore each concept in detail, including key characteristics and examples of tools. We’ll also compare observability vs monitoring vs telemetry and discuss when it’s appropriate to use each.

If you were asked to evaluate how good crews were at fighting forest fires, what metric would you use? Would you consider it a regression on your firefighters’ part if you had more fires this year than the last? Would the size and impact of a forest fire be a measure of their success? Would you look for the cause—such as a person lighting it, an environmental factor, etc—and act on it? Chances are that yes, that’s what you’d do.