How Dynatrace saves you from broken Kubernetes clusters

Like any other technology transformation, k8s adoption typically starts with small “pet projects”. One #Kubernetes (k8s) cluster here, another one over there. If you don’t pay attention, you may end up like many organizations these days, something that spreads like wildfire: hundreds or thousands of k8s clusters, owned by different teams, spread across on-premises and in the cloud, some shared, some very isolated.
As you scale your k8s clusters you need to enforce best practices on quotas and limits. You need to invest in standardizing observability to ensure your deployed services and applications are within your SLOs. You need to find a way to collect and store traces, logs, events, and metrics at scale and provide access to this data to DevOps and SREs when it comes to troubleshooting problems.
In this Observability Clinic Henrik Rexed, Cloud Native Advocate at #Dynatrace will remind us about the importance of observability in the k8s landscape. He will walk us through two real-production horror stories related to Kubernetes. For each story he will:
· Explain how we could avoid this situation by applying Kubernetes Best Practices
· Show the various features of Dynatrace that could save our lives from a broken cluster
Here are the links Henrik showed in the tutorial:
IsItObservable: isitobservable.io/
GitHub Repo: github.com/dynatrace-perfclin...
Dynatrace Trial: bit.ly/dtsaastrial
Chapter List:
00:00 - Introduction
01:03 - What you're learning today
02:02 - Introduction to ServiceMesh
05:10 - First k8s Crash Story
12:40 - How Dynatrace helps
21:00 - Live Demo #1
28:00 - Second k8s Horror Story
34:50 - Live Demo #2
41:25 - Consumption Monitoring with Kepler
45:10 - Key Take Aways and Wrap Up
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[@raynunezpena3655]

I love it. 😀