Kubernetes provides abstraction and simplicity with a declarative model to program complex deployments. However, this abstraction and simplicity create complexity when debugging microservices in this abstract layer. The following four vectors make it challenging to troubleshoot microservices.
Today, DevOps and SRE teams must stitch together an enormous amount of data from multiple, disparate systems that monitor infrastructure and services layers in order to troubleshoot Kubernetes microservices issues. Not only is it overwhelming to stitch this data, but troubleshooting using this data requires an understanding of monitoring systems at different levels of the stack. The result is that DevOps teams spend an enormous amount of time troubleshooting Kubernetes microservices issues.
Given the complex nature of Kubernetes microservices deployments and the overwhelming amount of data generated, without machines to help you diagnose and troubleshoot, it just may not be humanly possible. And certainly not in an environment where companies are running mission-critical applications, some customer-facing, where application outages or bad customer experience can cause a significant revenue impact. This problem is only getting worse by the day, given the density of applications and the dynamic nature of the computing environment.
We’ve spent a lot of time listening to you, our community, and our customers to better understand what you need to secure, observe, and troubleshoot your mission-critical microservices running on Kubernetes. We have synthesized countless hours of feedback to deliver a new product offering designed and built exclusively for Kubernetes that will enable SREs, DevOps engineers, and service owners to secure, observe, and troubleshoot Kubernetes deployments across heterogeneous environments.
Join me for this exclusive event where we will share the details of our new product offering.
Related content: Read our guide to Kubernetes monitoring
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