Operations | Monitoring | ITSM | DevOps | Cloud

Observability and monitoring are not about gathering different data—they differ in their purpose, but share the same data. Monitoring is focused on notification based on predefined questions. Whether that’s through Dashboards people watch, or push-based alerts to notification systems like SMS or purpose-built platforms like PagerDuty.

You've heard the term thrown around in meetups and Slack channels, but what exactly is full-stack observability? Simply put, you can see, understand, and quickly act on everything happening across your entire tech stack—from frontend user interactions to backend services, cloud infrastructure, and third-party integrations. Full-stack observability isn't just another tech buzzword. It's the difference between being blindsided by outages and catching issues before your users tweet about them.

I’ve increasingly changed my perspective on LLMs and modern AI systems over the past few years: Let me elaborate on why I believe this now.

Observability is critical for web operations to ensure that the application is working as expected and to identify any potential issues. However, setting up observability has traditionally been challenging because it can take hours to set up all the infrastructure, instrument your code and enable observability in production. But now there is a better way using native support for Django in Charmcraft and Rockcraft which has observability built in and ready to go!

Debugging software, whether during development or incident investigation, often begins with a manual and error-prone process. Developers typically scatter logs and snapshots across the codebase, allowing them to trigger multiple times. They then inspect the outputs and sift through the results to identify those relevant to the issue under investigation. Developers tend to group results that stem from the same user request or transaction.

In the fast-paced world of software development, debugging remains one of the most time-consuming and complex tasks for engineers. Modern observability tools that use logs, metrics, and traces help developers gain insights into system behavior, but they still require manual effort to identify and fix issues.

In today’s rapidly evolving technological landscape, observability has become essential for supporting robust, efficient systems. According to Gartner’s report “Preparing for the Future of Observability” from September 2024, OpenTelemetry (OTEL) is emerging as the standard framework for collecting telemetry data across different application pipelines.

If you’ve been using Honeycomb for a bit, you know that Calculated Fields (otherwise known as derived columns) are a powerful way to transform your events to a format that’s easier to query and understand. However, they use a lisp-esque language that can be difficult to read and a pain to write. If you dislike making Calculated Fields and want something a little easier, here’s a generative AI prompt that can generate them from natural language.

In a recent survey, it was discovered that 57% of software developers’ time is spent in meetings resolving performance problems rather than innovating software solutions. The culprit? A lack of full-stack observability. Without the right tools, IT teams are left playing a high-stakes game of “Guess That Outage” – leading to delayed response to critical incidents and excessive time spent in intense meetings focused on these incidents and their root cause.

Robust traffic analysis capabilities are essential to optimizing bandwidth in your environment. Before you implement, make sure your observability solution has these covered.