Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Observabilty for complex systems and related technologies.

Splunk Synthetics in Observability Terraform Provider Released

“How do you know your web properties and APIs are up and functioning as expected for users, not just nationally, but across the entire planet?“ Splunk Synthetic Monitoring provides an effective solution to monitor and track the reliability of web properties from locations all over the globe. By generating simulated user or API requests with Splunk Synthetics you’ll quickly be able to measure response times from various locations, devices, and connection types.

Twelve-Factor Apps and Modern Observability

The Twelve-Factor App methodology is a go-to guide for people building microservices. In its time, it presented a step change in how we think about building applications that were built to scale, and be agnostic of their hosting. As applications and hosting have evolved, some of these factors also need to. Specifically, factor 11: Logs (which I’d also argue should be a lot higher up in the ordering).

Elastic Observability: Built for open technologies like Kubernetes, OpenTelemetry, Prometheus, Istio, and more

As an operations engineer (SRE, IT Operations, DevOps), managing technology and data sprawl is an ongoing challenge. Cloud Native Computing Foundation (CNCF) projects are helping minimize sprawl and standardize technology and data, from Kubernetes, OpenTelemetry, Prometheus, Istio, and more. Kubernetes and OpenTelemetry are becoming the de facto standard for deploying and monitoring a cloud native application.

Trace at Your Own Pace: Three Easy Ways to Get Started with Distributed Tracing

Stepping through a trace is an invaluable debugging workflow, providing a way to follow requests from service to service even as the applications we manage become more complex and distributed. That same complexity can make getting started with distributed tracing feel overwhelming, but it’s important to remember that instrumenting your code is an additive process—you don’t need to boil the ocean. A trace through a thousand services starts with a single ID.

Learn How NS1 Uses Distributed Tracing to Release Code More Quickly and Reliably

Chris Bertinato, Software Architect at NS1, and Nate Daly, Head of Architecture at NS1 along with Jessica Kerr, Honeycomb Developer Advocate, and Account Executive Scott Phillips discuss how NS1 used distributed tracing to scale their organization and accelerate their migration from a monolith to microservices.

Discover Unknown Service Interaction Patterns With Istio & Honeycomb

Istio service meshes enable organizations to secure, connect, and monitor microservices to modernize their enterprise apps more swiftly and securely. With the addition of distributed tracing and powerful observability tooling, platform operators can gain immediate actionable insights about their applications.

Intercom: Building a More Resilient Ecosystem Through Observability

Learn how Intercom implemented Honeycomb’s distributed traces to learn about production. Kesha Mykhailov, Product Engineer at Intercom joins Honeycomb Developer Advocate Jessica Kerr, and Account Executive Michael Wilde to discuss how Intercom uses distributed traces to streamline their observability workflows, allowing their product engineers to learn about and from their production to increase Intercom’s resilience. Topics include.

Reference Architecture Series: Scaling Syslog

Join Ed Bailey and Ahmed Kira as they go into more detail about the Cribl Stream Reference Architecture, with a focus on scaling syslog. In this live stream discussion, Ed and Ahmed will explain guidelines for how to handle high volume UDP and TCP syslog traffic. They will also share different use cases and talk about the pros and cons for using different approaches to solve this common and often painful challenge.

See How Coveo Engineers Reduced User Latency

Many teams are wasting far too much time and energy searching through massive amounts of log data trying to find answers to user latency issues. Metrics data doesn’t help either as it only tells you that there is a problem, not where to fix it. This is why Coveo turned to observability. Through implementing observability with Honeycomb, Coveo was able to reduce their user latency by 50 percent.