Operations | Monitoring | ITSM | DevOps | Cloud

Alerts that you receive from your observability tool are based on conditions that existed seconds to minutes in the past, because the alert is only triggered after the data has been indexed within the tool. This means that your ability to take timely action in response to the condition is significantly limited, and often your window of opportunity to react is past by the time you receive the alert.

It’s 10:00 p.m. on a Sunday when my 9th grader bursts into my room in tears. She says she doesn’t understand anything about algebra and is doomed to fail. I jump into supermom mode only to discover I don’t remember anything about high school math. So, I do what any supermom does in 2024 and head to ChatGPT for help. These generative AI chatbots are amazing. I quickly get a detailed explanation of how to solve all her problems.

The OpenTelemetry project is changing how organizations approach observability. It aims to standardize monitoring across different systems. OpenTelemetry—commonly referred to as OTel—provides APIs, SDKs, exporters, and collectors. It is making data collection, analysis, and utilization more efficient, leading to better decision-making and technology adoption.

Remember the Tabs vs. Spaces arguments? It seems that observability has grown up enough that we are arguing over which signals are the “best” signals for observability. Often referred to as the Pillars of Observability, Metrics, Logs, and Traces (sometimes adding Events for MELT) each provide a unique perspective on a system. What happens when we change our perspective from finding the “best” telemetry format to finding the telemetry that aligns with the problems we need to solve?

In an increasingly digital world, organizations face complex challenges in managing their security data that’s growing at a relentless pace. With the rapid growth of cyber assets and the ever-present threat of sophisticated attacks, legacy security tools often struggle to keep up.

tldr: This post discusses how to measure CDN request locality without indexing a single log. The function of a CDN is to bring cacheable data, like pictures & videos, physically closer to your customers. This reduces latencies, and load on your backend servers. When measuring CDNs, there are some very common metrics.

Chaos Monkey is an open-source tool. Its primary use is to check system reliability against random instance failures. Chaos Monkey follows the testing concept of chaos engineering, which prepares networked systems for resilience against random and unpredictable chaotic conditions. Let’s take a deeper look.