GCP-GKE: Architecting with Google Kubernetes Engine

$1,800.00

  • Duration: 3 Days
  • Mode of Delivery: Online -Instructor-led training
  • Level: Intermediate
  • Job role: Administrator
  • Preparation for exam: None
  • Cost: USD$1,800.00

This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. This Architecting with Google Kubernetes Engine course is available as a live Virtual Classroom and will run over three consecutive days.

Enquire for a course date

20 in stock

SKU: AZ-303-1-1-2-2-1-1-1-1-1-1-1-1-1 Categories: , , , , ,

Audience

This course is suitable for Cloud architects, administrators, and those working in SysOps/DevOps. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform will also benefit from this course with a focus on Compute Engine
• Systems operations experience, including deploying and managing applications, either on-premises or in a public cloud environment
• Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs
• Creating and maintaining machine learning and statistical models
• Querying datasets, visualizing query results and creating reports

Prerequisites

To get the most out of this course, you should have:
• Completed the Google Cloud Platform Fundamentals or have equivalent experience
• Basic proficiency with command-line tools and Linux operating system environments

Skills Gained

To get the most out of this course, you should have:
• Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms
• Explain how software containers work and the architecture of Kubernetes
• 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
• Manage 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

Course outline

Module 1: Introduction to Google Cloud Platform
• The Google Cloud Platform Console
• Cloud Shell
• Define cloud computing
• Identify GCPs compute services
• Regions and zones
• The cloud resource hierarchy
• Administer your GCP resources

Module 2: 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

Module 3: 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

Module 4: Kubernetes Operations
• Work with the kubectl command
• Inspect the cluster and Pods
• View a Pods console output
• Sign in to a Pod interactively

Module 5: 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

Module 6: GKE Networking
• Create Services to expose applications that are running within Pods
• Use load balancers to expose Services to external clients
• Create Ingress resources for HTTP(S) load balancing
• Leverage container-native load balancing to improve Pod load balancing
• Define Kubernetes network policies to allow and block traffic to pods

Module 7: Persistent Data and Storage
• Use Secrets to isolate security credentials
• Use ConfigMaps to isolate configuration artifacts
• Push out and roll back updates to Secrets and ConfigMaps
• Configure Persistent Storage Volumes for Kubernetes Pods
• Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts

Module 8: 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
• Define Kubernetes pod security policies
• The structure of GCP IAM
• IAM roles and policies for Kubernetes Engine cluster administration

Module 9: Logging and Monitoring
• Use Stackdriver to monitor and manage availability and performance
• Locate and inspect Kubernetes logs
• Create probes for wellness checks on live applications

Module 10: Using GCP Managed Storage Services from Kubernetes Applications
• 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

Schedule

Click on the following link to see the current Course Schedule
Our minimum class-size is 3 for this course.
If there are no scheduled dates for this course, it can be customized to suit the time and skill needs of clients and it can be held online, at a rented location or at your premises.
Click on the following link below to arrange for a custom course: Enquire about a course date

Product Information

Kubernetes is a container orchestration system that was initially designed by Google to help scale containerized applications in the cloud. A container is a way of packaging software that makes it easy to run that software on any platform, ranging from your laptop to a server in a datacenter to a cluster running in the public cloud. A container orchestrator is a software platform that makes it easy to run many thousands of containers on top of thousands of machines. Kubernetes can manage the lifecycle of containers, creating and destroying them depending on the needs of the application, as well as providing a host of other features. Kubernetes has become one of the most discussed concepts in cloud-based application development, and the rise of Kubernetes signals a shift in the way that applications are developed and deployed.
In general, Kubernetes is formed by a cluster of servers, called Nodes, each running Kubernetes agent processes and communicating with one another. The Master Node contains a collection of processes called the control plane that helps enact and maintain the desired state of the Kubernetes cluster, while Worker Nodes are responsible for running the containers that form your applications and services.


GKE: GKE is a CaaS offering. It allows you to create Kubernetes clusters on demand, which takes away all of the heavy lifting of installing the clusters yourself. It leverages Google Compute Engine for hosting the cluster nodes, but the customer does not need to bother with the infrastructure and can concentrate on writing the code. The provision cluster can be automatically updated and scaled. The Google Cloud Platform (GCP) software-defined networks are integrated with GKE and allow users to create network objects, such as load balancers, on demand when the application is deployed. Several services integrate with GKE, such as a container repository, which allows you to store and scan your container images.

Additional Information and FAQs

CERTFICATE OF COMPLETION: Participants will receive a certificate of completion at the end of a course. This is not an official certification for the product and/or software. Our courses do indicate the appropriate certification exam(s) that the participant can sit. Data Vision Systems does not provide certification or deliver the certification exams. Participants are responsible for arranging and paying for the certification exams on the appropriate certification body.

CANCELLATION POLICY: There is never a fee for cancelling seven business days before a class for any reason. Data Vision Systems reserves the right to cancel any course due to insufficient registration or other extenuating circumstances. Participants will be advised prior to doing so.

Reviews

There are no reviews yet.

Be the first to review “GCP-GKE: Architecting with Google Kubernetes Engine”

Your email address will not be published. Required fields are marked *

Related Courses