Monitoring the current state of system components is one of the most efficient ways to identify problems and bottlenecks in a production environment. Administrators working with Kubernetes need to know how to monitor at multiple levels, including at the node, cluster, and Pod levels. In this course, you'll examine the vital Kubernetes performance metrics to monitor, what to measure in Kubernetes clusters, nodes, and Pods, and best practices for monitoring these. You'll also identify the differences between logging and monitoring. Moving on, you'll write configuration files to create and start node-problem-detector. You'll then specify Pod configuration files to ensure kubelet performs liveness probes, HTTP GET requests are sent to the server running in the container, and TCP liveness probe readiness is defined. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in preparation for it.

Kubernetes provides the capability to dynamically scale deployments in order to scale the number of Pods in a deployment up or down and add resiliency using various commands and tools. In this course you'll recognize what a DaemonSet is, the patterns for communicating with the Pods in a DaemonSet, the role of Kubernetes Garbage Collector, and the resource types and resource units used in Kubernetes. You'll review the commands used to manage Kubernetes objects, and the features of tools used for templating YAML in Kubernetes.Next, you'll create a deployment and scale the deployment, scale StatefulSets and make in-place updates, and create a defined Horizontal Pod Autoscaler and DaemonSet. Moving on, you'll create jobs and list all the Pods that belong to the jobs. You'll see how to deploy a replicated topology with a StatefulSet controller, simulate Pod and Node downtime, and implement a case study of cascading deletion and managing orphans dependents. Finally, you'll use the Kustomize tool to demonstrate the composition of deployment and service resources and install Helm and Service Catalog in Kubernetes clusters. This course is part of a series that aligns with the objectives for the Certified Kubernetes Administrator exam and can be used to prepare for this exam.

Kubernetes classifies services into different types to facilitate restricting services within clusters, expose them on static ports, enable the use of cloud providers' load balancer and facilitate load balanced and scalable Pods. In this course you'll investigate the primary modes for finding a Service supported by Kubernetes, the essential components used to implement automation of Service Accounts, the features provided by Service Catalog, the steps for listing Managed Services and Plans of a Service Broker and how to provision a new instance of a Managed Service. Next, you'll create pods running a NGINX cluster side address space and NGINX replicas, and create a service of types ClusterIP, NodePort, LoadBalancer and ExternalName. Finally, you'll visualize Service Account in the namespace, create additional Service Account objects and retrieve a dump of the Service Account object, modify the default Service Account for a namespace, use Secrets as imagePullSecret and also create, describe, delete and invalidate API tokens for a Service Account. This course is part of a series that aligns with the objectives for the Certified Kubernetes Administrator exam and can be used to prepare for this exam.

Kubernetes doesn't provide any native storage solution for log data, but it can be integrated with existing logging solutions. You can classify Kubernetes logs and manage them at the application and cluster level to enable observability and identify the root cause of issues. In this course, you'll learn how to do just that. You'll kick off by exploring the Logging architecture of Kubernetes clusters, the different log types maintained in Kubernetes clusters, the Kubernetes architecture that helps manage logging at the node and cluster level, best practices for Kubernetes logging, the role of the Resource Metrics pipeline, and the architecture of Metric server. Next, you'll configure a Pod specification with a container to write logs to standard output every 5 seconds and return snapshot logs from various Pod types. Moving on, you'll configure a Pod to write two different log files using different formats, create a Pod with two sidecar containers, and inspect, debug, and get logs from applications. Finally, you'll install the Metric server and use it to manage metrics for Nodes and Pods. You'll view Pod and Node resource metrics, create a Pod with one container, attach a Shell, and then execute commands in it. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in its preparation.

Cloud-hosted Kubernetes clusters benefit from underlying cloud capabilities but are not immune to issues related to workload management and Pod availability. Administrators need to investigate and swiftly resolve such issues by adopting the most efficient and effective troubleshooting approaches. In this course, you'll examine the recommended options for customizing Kubernetes clusters, how extension points interact with Kubernetes Control Plane, the log files present on a cluster's master and worker nodes, and master and worker node critical entries that help identify issues. To complete this course, you'll practice resolving potential problems associated with using Google Kubernetes Engine (GKE) and fix common issues related to deployed workloads on Google Kubernetes clusters. Lastly, you'll set up a Kubernetes cluster to ingest logs into Elasticsearch and visualize the metrics using Kibana. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in preparation for it.

Troubleshooting is a critical yet complicated task in Kubernetes. Monitoring the right metrics and setting alerts is essential to identify an issue's root cause. In this course, you'll learn to dig deep and investigate every component of a Kubernetes cluster, uncovering the root causes of common Kubernetes issues and taking appropriate action to troubleshoot them. Some of the items you'll explore include the Kubernetes components to consider when planning troubleshooting, the commands for troubleshooting cluster components, techniques for troubleshooting etcd database, and the critical problems and recommended troubleshooting approaches associated with managing Pods. Items you'll practice troubleshooting include issues associated with kubeadm installation, Kubernetes cluster components, Kubernetes resource configuration, Kubernetes Services, Pods remaining in a pending and unschedulable state, and Pod failure CrashLoopBackOff scenarios. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in preparation for it.

Reliable networking and deployments are critical in enabling robust and highly-available Kubernetes clusters. Continuous monitoring and the right diagnostic tools and commands help analyze network latency, performance, and availability. In this course, you'll explore the built-in Kubernetes application debugging mechanism. Furthermore, you'll examine how to manage out-of-resource scenarios and frequently encountered Kubernetes deployment networking issues. Moving along, you'll configure ReplicationController to ensure Pods are always up and apply configuration files to scale up applications and manage load. You'll use a Pod as a test environment to diagnose DNS problems and investigate Kubernetes network ingress, service traffic flows, and init container execution problems. You'll inspect Kubernetes iptables rules used for virtual IP translation and Service IP load balancing and gracefully delete StatefulSet Pods. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in preparation for it.

Kubernetes infrastructure is a combination of multiple resources that includes servers, physical or virtual machines, cloud platforms, and more. Kubernetes architecture and components facilitate dynamic and on-demand provisioning of resources that are required to deploy Kubernetes clusters and their components. In this course, you'll explore the key considerations for Kubernetes deployment and review the pros and cons of the best known approaches for deploying Kubernetes. You'll learn about node features, the recommended approach to provision and add nodes to Kubernetes clusters, and the process of upgrading Kubernetes clusters. Next, you'll explore how to use kubectl commands to view a node's status, configure the default CPU requests and limits for namespaces, set quotas for total amount of memory and CPU for all containers running in a namespace, set quotas for total number of Pods that can run in a namespace, and set minimum and maximum values for CPU resources being used by containers and pods in a namespace. Moving on, you'll use kubeadm and kubectl commands to upgrade cluster control plane nodes from version 1.18.x to version 1.19.x and upgrade kubelet and kubectl on all cluster control plane nodes from version 1.18.x to version 1.19.x. Finally, you'll discover how to upgrade kubeadm, kubelet, and kubectl on all cluster worker nodes from version 1.18.x to version 1.19.x. This course is part of a series that aligns with the objectives for the Certified Kubernetes Administrator exam and can be used to prepare for this exam.

Kubernetes manages Volumes and Containers by defining low level representation of a StorageVolume using the PersistentVolume object and binding Pod and PersistentVolume using PersistentVolumeClaim. In this course you'll recall the applicable Reclaim policies and features for PersistentVolume, the concept of Node Affinity, the various phases of a Volume, the maximum number of volumes that can be attached to a Node and the approach of configuring custom limits. Next, you'll create a storage device using AWS EBS and a PersistentVolume that uses the EBS, create a PersistentVolumeClaim to use the PersistentVolume of AWS EBS storage device, create a PersistentVolume with an EBS via AWS API and mount it to AWS EC2 instance. You'll also create a Pod that uses Dynamic PersistentVolumeClaim, delete PersistentVolume and PersistentVolumeClaim, change the Reclaim policy from Retain to Delete for PersistentVolume and create a PersistentVolume using an existing Compute Engine persistent disk. Finally, you'll create a Pod with two containers to exchange data, create a deployment that uses a PersistentVolumeClaim to mount it as a Volume and create a Pod to mount the NFS Volume in a Docker container to the master node. This course is part of a series that aligns with the Certified Kubernetes Administrator exam's objectives and can be used in its preparation.