With incredibly complex cloud architecture — that may even includes Kubernetes and multi-tenant infrastructure — organizations are finding it hard to measure and monitor the performance and cost of their cloud environments. To stay competitive, organizations should aim to be as efficient as possible — which can help companies to lower costs, increase margins, and improve cloud efficiency.

For our Rails/Rack customers, the latest Ruby release v3.1.0 is now available. This is brand new, and Gems are still catching up, so please ensure your application works with this version locally before upgrading. For more information, see the official release notes.

Big data cloud performance management is key to success in the cloud. Paired with cloud computing, big data can transform an enterprise—especially when managed correctly. It requires no CapEx, enables quicker data processing and analysis, and allows for rapid scalability. But not having a plan to properly manage your big data performance in the cloud can be the difference between realizing the ROI the cloud promises and having to move back to the data center in defeat.

If you have set up your own monitoring services with Amazon CloudWatch, Azure Monitor or another internal tool, we suggest you consider looking beyond the horizon. These services often provide internal web monitoring only. Perhaps they validate HTTP availability from locations outside their networks, but HTTP checks won’t give you a 360º view into the state of your services.

Spot Ocean delivers container-driven autoscaling to continuously monitor and optimize your cloud environment. Positioned at a busy crossroads in the application deployment pipeline, Ocean has a critical role when shipping new containers. Given the highly dynamic nature of Kubernetes environments, events happen constantly and take shape as logs in Ocean. These can help you understand the chain of events in different scaling scenarios, from debugging cluster issues to incident analysis.

Whether you’re using a managed Kubernetes service like AWS EKS, GCP GKE or Azure AKS, or self-managing a DIY cluster deployed with open source tools like kops and Kubespray, the underlying hardware can vary from container to container. Each container requires specific resources (CPU/ memory/GPU/network/disk) and as long as the underlying infrastructure can provide those resources, the container will be able to execute its business logic.

I recently had a cloud migration client who was at the beginning stage of their discovery phase and looking to jump straight to “which database platforms should I be using in the cloud?” - a tall ask you might say, but following the three steps below they were able to discover and analyze all of their database servers in just two weeks.

Just like that, another year has gone by full of remote work, virtual conferences, and lengthy Zoom calls. And, although we were not able to see our fellow Splunkers in person at.conf21 that didn’t stop us from previewing the latest enhancements to Splunk Enterprise Security. And now, it gives us great pleasure to announce that Enterprise Security 7.0 is available!