In 2021, any time that you access any kind of web service, whether it be via a website or app, chances are high that the backend is running on Kubernetes. Hundreds of thousands of organizations rely on Kubernetes to power and manage their mission critical services every day, and the reliability and scalability benefits offered by Kubernetes have been felt across the industry.

In the computing world, Observability and Monitoring has got an important place. The recent days have witnessed a great use of these terms in the IT infrastructure and among the developers, as observability and monitoring have been extremely effective in tracking the events. Both systems are intertwined to each other, but there is a small line of separation among the two. What? Why? What's the difference? These are the questions that has to be answered. Let's figure out the reasons here.

Observability is one of the most popular topics in technology at the moment, and that isn’t showing any sign of changing soon. Agentless log collection, automated analysis, and machine learning insights are all features and tools that organizations are investigating to optimize their systems’ observability. However, there is a new kid on the block that has been gaining traction at conferences and online: the Extended Berkeley Packet Filter, or eBPF. So, what is eBPF?

“Work from anywhere” is now a common occurrence. With so many companies now dependent on a distributed workforce, IT teams need to be able to quickly diagnose and troubleshoot WiFi problems. Moreover, they, themselves, are often working remotely. In order to successfully do their jobs, consistent WiFi is obviously essential for remote workers.

Today, I found a bug before I noticed it. Like, it was subtle, and so I wasn’t quite sure I saw it—maybe I hadn’t hit refresh yet? Later, I looked at the trace of my function and, boom, there was a clear bug. Here’s the function with the bug. It responds to a request to /win by saving a record of the win and returning the total of my winnings so far. Can you spot the problem in the TypeScript? It’s subtle. Now here’s a trace in Honeycomb: Now do you see the bug?

Elastic Observability 7.16 introduces curated data exploration views for ad hoc analysis and further extends visibility into complex and distributed systems with the general availability (GA) of dozens of prebuilt Elastic Agent data integrations, observability tooling for continuous integration and continuous delivery (CI/CD) pipelines, and a new native data source integration with Amazon Web Services (AWS) FireLens. These new features allow customers to.

Without unified observability, it’s stressful not having complete visibility into your application. Plus, it contributes to risky deployments. Yet we hear that many developers have poor visibility into what powers production code. Without transparency into their apps, developers cannot see: You can try navigating tickets, permissions, and dashboards that don’t tell the right story, but there are ways to solve this problem.

I have a good sense of how to use traces to understand my system’s behavior within request/response cycles. What about multi-request processes? What about async tasks spawned within a request? Is there a higher-level or more holistic approach?

I can’t remember the last time I drove down highway 101 between San Francisco and the South Bay and didn’t see a billboard claiming to be the single tool to solve all of my data problems.