This Architecting with Google Kubernetes Engine course introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, students explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring.
This course is intended for the following participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform.
To get the most out of this course, participants should have: Completed Google Cloud Platform Fundamentals: Core Infrastructure or have equivalent experience Basic proficiency with command-line tools and Linux operating system environments
Introduction to Google Cloud Platformul> Use the Google Cloud Platform Console/li> Use Cloud Shell/li> Define cloud computing/li> Identify GCPs compute services/li> Understand regions and zones/li> Understand the cloud resource hierarchy/li> Administer your GCP resources/li>Containers and Kubernetes in GCPul> Create a container using Cloud Build/li> Store a container in Container Registry/li> Understand the relationship between Kubernetes and Google /li> Kubernetes Engine (GKE)/li> Understand how to choose among GCP compute platforms/li>Kubernetes Architectureul> Understand the architecture of Kubernetes: pods, namespaces/li> Understand the control-plane components of Kubernetes/li> Create container images using Google Cloud Build/li> Store container images in Google Container Registry/li> Create a Kubernetes Engine cluster/li>Kubernetes Operationsul> Work with the kubectl command/li> Inspect the cluster and Pods/li> View a Pods console output/li> Sign in to a Pod interactively/li>Deployments, Jobs, and Scalingul> Create and use Deployments/li> Create and run Jobs and CronJobs/li> Scale clusters manually and automatically/li> Configure Node and Pod affinity/li> Get software into your cluster with Helm charts and Kubernetes Marketplace/li>GKE Networking ul> Create Services to expose applications that are running within Pods/li> Use load balancers to expose Services to external clients/li> Create Ingress resources for HTTP(S) load balancing/li> Leverage container-native load balancing to improve Pod load balancing/li> Define Kubernetes network policies to allow and block traffic to pods/li>Persistent Data and Storageul> Use Secrets to isolate security credentials/li> Use ConfigMaps to isolate configuration artifacts/li> Push out and roll back updates to Secrets and ConfigMaps/li> Configure Persistent Storage Volumes for Kubernetes Pods/li> Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts/li>Access Control and Security in Kubernetes and Kubernetes Engineul> Understand Kubernetes authentication and authorization/li> Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces/li> Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources/li> Define Kubernetes pod security policies/li> Understand the structure of GCP IAM/li> Define IAM roles and policies for Kubernetes Engine cluster administration/li>Logging and Monitoringul> Use Stackdriver to monitor and manage availability and performance/li> Locate and inspect Kubernetes logs/li> Create probes for wellness checks on live applications/li>Using GCP Managed Storage Services from Kubernetes Applicationsul> Understand pros and cons for using a managed storage service versus self-managed containerized storage/li> Enable applications running in GKE to access GCP storage services/li> Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application/li>