Unlocking the full potential of monitoring through ML integration, anomaly detection, and innovative scoring engines. Machine Learning has been making waves in various industries, but its adoption in the monitoring and observability space has been slower than expected. Many “ML” features remain gimmicky and do not provide actual real world value to users that encourages their further use.
How frustrating is it when you’ve just landed on a web page, you click on a certain element and an ad or something else pops up and you end up clicking that thing instead? That’s a layout shift, which is bad for the user’s experience and the later they happen, the worse it is. Research from HTTP Archive shows that over 80% of websites use web fonts. Web fonts also cause layout shifts, if they’re not being loaded strategically.
The use of statistics, advanced algorithms and AI/Ml is becoming omnipresent. The benefits are visible in every walk of life, from web searches, to movie and retail recommendations, to auto-completing our emails. Of course, not many anticipated the dramatic entrance of generative AI in the form of ChatGPT for writing college essays and poetry on arcane topics.
The Canvas panel, which will be Generally Available in Grafana 10, combines the power of Grafana with the flexibility of custom elements. Canvas visualizations are extensible, form-built panels you can use to explicitly place elements within static and dynamic layouts. This empowers you to design custom visualizations and overlay data in ways that aren’t possible with standard Grafana panels, all within Grafana’s UI.
As a big proponent of open source and all things open, I jumped at the opportunity to expand on Cribl Stream’s OpenTelemetry implementation. I’m happy to report that as of Cribl Stream 4.1, both our OpenTelemetry source and destination now support OTLP over HTTP!
A web application or an API breaking is a matter of when, not if. Whether the cause is buggy code making it to production or infrastructure failing to support the software built upon it, incidents of varying severity are the norm rather than the exception, appearing frequently enough that the industry has coined the terms Mean Time To Detect (MTTD) and Mean Time To Recovery (MTTR).
At Logz.io, we’re seeing a very fast pace of adoption for Kubernetes–at this point, it’s even outpacing cloud adoption, with companies running on-prem fully adopting Kubernetes in production. Why are companies going in this direction? Kubernetes provides additional layers of abstraction, which helps create business agility and flexibility for deploying critical applications. At the same time, those abstraction layers create additional complexity for observability.
A while ago, we added Metrics to our observability platform so teams could easily see system information right next to their application observability data—no tool or team switching required. So how can teams get the most out of metrics in an observability platform? We’re glad you asked! We had this conversation with experts at Heroku. They’ve successfully blended metrics and observability and understand what is most helpful to know.
