- Stay within resource usage requirements.
- Do not engage in or encourage activity that is illegal.
- Do not engage in cryptocurrency mining.
The hands-on lab is part of these learning pathsSee 1 more
Ready for the real environment experience?
When it comes to running production-grade Kubernetes clusters, monitoring and alerting are considered essential components of an enterprise Kubernetes observability stack.
In this hands-on lab, you'll learn how to set up and use the following essential monitoring applications:
You'll learn how to integrate these monitoring applications together into an effective and cohesive monitoring solution.
Upon completion of this Lab, you will be able to:
- Deploy and instrument a sample Python Flask web-based API into Kubernetes, instrumented to provide metrics which will be collected by Prometheus and displayed within Grafana
- Install and configure the Kubernetes Dashboard using Helm
- Install and configure Prometheus into Kubernetes using Helm
- Setup Prometheus for service discovery
- Install and configure Grafana into Kubernetes using Helm
- Import a pre-built dashboard for real-time visualisations
This lab is intended for:
- Kubernetes practitioners
- DevOps Engineers
You should be familiar with:
- Basic Linux command line administration
- Basic Kubernetes and Container-based concepts
This Lab will start with the following AWS resources provisioned automatically for you:
- 2 x EC2 instances - each assigned a public IP address:
- ide.cloudacademy.platform.instance - provides a web-based IDE with integrated terminal
- k8s.cloudacademy.platform.instance - provides a fully functional Kubernetes cluster
To achieve the Lab end state, you will be walked through the process of:
- Using your local workstation browser to remotely connect to the ide.cloudacademy.platform.instance
- Using the web-based IDE and integrated terminal, you'll complete the remainder of the stated Lab Objectives (above)
Jeremy is a Content Lead Architect and DevOps SME here at Cloud Academy where he specializes in developing DevOps technical training documentation.
He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 25+ years. In recent times, Jeremy has been focused on DevOps, Cloud (AWS, GCP, Azure), Security, Kubernetes, and Machine Learning.
Jeremy holds professional certifications for AWS, GCP, Terraform, Kubernetes (CKA, CKAD, CKS).