Google Kubernetes Engine Training Overview
This Architecting with Google Kubernetes Engine training course teaches attendees how to manage and deploy containerized applications on Google Kubernetes Engine (GKE) and other services provided by Google Cloud. Students learn how to deploy solution elements, including infrastructure components such as pods, containers, deployments, and services. In addition, participants learn practical solutions for security and access management, resource management, and resource monitoring.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, we offer some courses as live, instructor-led online classes for individuals.
Objectives
- Understand how software containers work
- Understand the architecture of Kubernetes
- Understand the architecture of Google Cloud
- Understand how pod networking works in Kubernetes Engine
- Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/kubectl commands Launch
- Roll back and expose jobs in Kubernetes
- Manage access control using Kubernetes RBAC and Google Cloud IAM
- Managing pod security policies and network policies
- Use Secrets and ConfigMaps to isolate security credentials and configuration artifacts
- Understand GCP choices for managed storage services
- Monitor applications running in Kubernetes Engine
Prerequisites
All participants should have:
Outline
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Introduction to Google Cloud Platform
- The Google Cloud Platform Console
- Cloud Shell
- Cloud computing
- GCPs compute services
- Regions and zones
- The cloud resource hierarchy
- Administer your GCP resources
Containers and Kubernetes in GCP
- Create a container using Cloud Build
- Store a container in Container Registry
- The relationship between Kubernetes and Google Kubernetes Engine (GKE)
- How to choose among GCP compute platforms
Kubernetes Architecture
- The architecture of Kubernetes: pods, namespaces
- The control-plane components of Kubernetes
- Create container images using Google Cloud Build
- Store container images in Google Container Registry
- Create a Kubernetes Engine cluster
Kubernetes Operations
- Work with the kubectl command
- Inspect the cluster and Pods
- View a Pods console output
- Sign in to a Pod interactively
Deployments, Jobs, and Scaling
- Create and use Deployments
- Create and run Jobs and CronJobs
- Scale clusters manually and automatically
- Configure Node and Pod affinity
- Get software into your cluster with Helm charts and Kubernetes Marketplace
GKE Networking
- Create Services to expose applications that are running within Pods
- Load balancers to expose Services to external clients
- Create Ingress resources for HTTP(S) load balancing
- Container-native load balancing that improve Pod load balancing
- Kubernetes network policies that allow and block traffic to pods
Persistent Data and Storage
- Secrets to isolate security credentials
- ConfigMaps to isolate configuration artifacts
- Push out and roll back updates to Secrets and ConfigMaps
- Configure Persistent Storage Volumes for Kubernetes Pods
- StatefulSets to ensure that claims on persistent storage volumes persist across restarts
Access Control and Security in Kubernetes and Kubernetes Engine
- Kubernetes authentication and authorization
- Kubernetes RBAC roles and role bindings for accessing resources in namespaces
- Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources
- Kubernetes pod security policies
- The structure of GCP IAM
- IAM roles and policies for Kubernetes Engine cluster administration
Logging and Monitoring
- Stackdriver to monitor and manage availability and performance
- Locate and inspect Kubernetes logs
- Create probes for wellness checks on live applications
Using GCP Managed Storage Services from Kubernetes Applications
- The pros and cons for using a managed storage service versus self-managed containerized storage
- Enable applications running in GKE to access GCP storage services
- Use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application
Conclusion
Training Materials
All GCP training students receive comprehensive courseware.
Software Requirements
Students must have a modern web browser (ideally Chrome) and Internet access.
