Architecting with Google Kubernetes Engine (AGKE)

Architecting with Google Kubernetes Engine (AGKE)

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Duration

3 Days

Certified Instructor

Course Id

nextecGC818

Course Description

Course Overview

The Architecting with Google Kubernetes Engine (AGKE) course is designed to provide individuals with the knowledge and skills necessary to design, deploy, and manage scalable and highly available applications on the Google Kubernetes Engine (GKE). This course focuses on architectural principles, best practices, and hands-on experience to enable participants to build robust and efficient solutions using Kubernetes on the Google Cloud Platform (GCP).

Prerequisites

To enroll in the AGKE course, participants should have a strong understanding of containerization concepts and familiarity with GCP fundamentals. Prior experience with deploying and managing applications in containerized environments will be beneficial. Participants should also have a basic understanding of networking, storage, and compute concepts.

Methodology

The AGKE course follows a blended learning approach, combining theoretical instruction, demonstrations, discussions, and hands-on labs. Participants will engage in instructor-led sessions where architectural principles and best practices for GKE are explained. They will also have access to GKE resources and tools to gain practical experience in designing and deploying applications. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Google Kubernetes Engine (GKE)

Overview of Kubernetes and GKE’s key features

Understanding GKE’s architecture and components

Exploring GKE’s networking and storage options

Designing Application Workloads in GKE

Containerizing applications and designing container images

Configuring resource requirements and limits for containers

Designing multi-container and multi-tier applications in GKE

Networking and Load Balancing in GKE

Configuring GKE cluster networking and subnets

Implementing internal and external load balancing with GKE

Designing network policies for application isolation and security

Scaling and Autoscaling Applications in GKE

Designing pod autoscaling based on CPU and custom metrics

Configuring cluster autoscaling based on workload demands

Utilizing horizontal pod autoscaling for application performance

Storage and Data Management in GKE

Utilizing persistent volumes and persistent volume claims

Designing for data redundancy and backup strategies

Utilizing cloud-native storage solutions with GKE

Securing and Managing GKE Clusters

Implementing security controls and best practices in GKE

Managing access to GKE clusters using IAM and RBAC

Monitoring and managing GKE clusters for availability and performance

Application Observability and Troubleshooting

Configuring and utilizing logging and monitoring with GKE

Troubleshooting common issues and performance bottlenecks

Implementing observability and distributed tracing for applications

Outcome

By the end of the AGKE course, participants will have:

  • Developed a comprehensive understanding of architectural principles and best practices for designing solutions on GKE
  • Acquired practical knowledge in designing and deploying containerized applications on GKE
  • Gained expertise in networking, load balancing, scaling, and storage solutions in GKE
  • Learned techniques for securing and managing GKE clusters effectively
  • Gained hands-on experience through practical labs and exercises
  • Prepared to design, deploy, and manage scalable and highly available applications using GKE on the GCP platform

Labs

The AGKE course includes hands-on labs that provide participants with practical experience in designing and deploying applications on GKE. Some examples of lab exercises include:

  • Creating and configuring GKE clusters with appropriate node pools and network settings
  • Deploying containerized applications to GKE clusters using Kubernetes manifests
  • Configuring networking and load balancing for GKE services
  • Implementing autoscaling for GKE deployments based on workload demands
  • Configuring persistent volumes and persistent volume claims for data storage
  • Implementing security controls and RBAC for GKE clusters

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in designing and deploying applications on GKE, allowing them to develop practical skills as architects in the GCP environment.

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