Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has changed the way many organizations approach the deployment of their applications. But despite its benefits, the additional layers of abstraction and reams of data can cause complexity around Kubernetes monitoring. We’ve seen so much of these challenges borne out in the results of the 2024 Observability Pulse survey. In the survey report, 36% of respondents say Kubernetes poses a challenge, and just 10% of organizations say they have full observability into their environments.

Amazon CloudWatch Logs enables you to centralize the logs from different AWS services, logs from your applications running in AWS and on-prem servers, using a single highly scalable service. You can then easily view these logs data, search them for specific error codes or patterns, filter them based on specific fields, or archive them securely for future analysis.

There is observation then there’s analysis. Log Analytics falls under the latter category. Observation and analysis are not mutually exclusive; one builds upon the other. Similarly, Log analytics advances beyond simple log monitoring, enabling observability teams to identify trends and irregularities throughout your enterprise. To demystify what is Log Analytics, let’s first have a look at the definition.

Back in September when Cisco announced they were acquiring Splunk, we explained how the market was consolidating with Sumo Logic ahead of the pack, challenging traditional vendors with our cloud-native platform. Now that the deal is complete and Splunk is officially a Cisco company, we’re hearing from more Splunk customers who are considering their options.

Like humans, machines need to continually learn from non-stationary information streams. While this is a natural skill for humans, it’s challenging for neural networks-based AI machines. One inherent problem in artificial neural networks is the phenomenon of catastrophic forgetting. Deep learning researchers are working extensively to solve this problem in their pursuit of AI agents that can continually learn like humans.

My introduction to the world of data science was writing anomaly detection for a SIEM that catered to banks and credit unions. Some of these places were running on 50-year-old IBM core banking servers — meaning that someone trying to turn off a light in a server room could take down an entire bank with a literal flip of the wrong switch. While some companies take their time updating infrastructure, others still embody the move-fast-and-break-things philosophy of the early dot-com era giants.