Getting Started with Kubernetes and Google Container Engine

Learn to containerize workloads and deploy to clusters.

Learn to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Container Engine, and scale those workloads to handle increased traffic. You also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates.

GK# 6591 Vendor# GCP-KUB

Vendor Credits:

Group Training

Group Training

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Who Should Attend?

Application Developers, Cloud Solutions Architects, DevOps Engineers, IT managers
Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform

What You'll Learn

This course teaches participants the following skills:

Understand container basics
Containerize an existing application
Understand Kubernetes concepts and principles
Deploy applications to Kubernetes using the CLI
Set up a continuous delivery pipeline using Jenkins

Course Outline

Module 1: Containers 101 and Docker

Create a container
Package a container using Docker
Store a container image in Google Container Registry
Launch a Docker container

Module 2: Kubernetes and Google Container Engine Basics

Provision a complete Kubernetes cluster using Google Container Engine
Deploy and manage Docker containers using kubectl
Break an application into microservices using Kubernetes’ Deployments and Services

Module 3: Deploying to Kubernetes

Create a Kubernetes deployment
Trigger, pause, resume, and rollback updates
Understand and build canary deployments

Module 4: Continuous Deployment with Jenkins

Provision Jenkins in your Kubernetes cluster
Create a Jenkins pipeline
Implement a canary deployment using Jenkins

Prerequisites

Basic proficiency with command line tools and Linux operating system environments as well as Web server technologies such as Nginx
Systems Operations experience including deploying and managing applications either on-premises or in a public cloud environment

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