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.