Kubernetes Containerization Training.

Kubernetes Containerization Training will help you learn how to run your own infrastructure with Kubernetes. Kubernetes can give you some of the same superpowers that the site reliability engineers at Google utilize to ensure that Google’s services are resilient, reliable, and efficient. Using Kubernetes allows you to make use of the knowledge and expertise that engineers at Google and other companies have built up by virtue of their massive scale.

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Kubernetes Containerization training is for anyone who wish to learn to use kubernetes to deploy and manage containers and clusters. You will learn basic architecture of kubernetes. Using AWS, Google and Microsoft cloud platform to run Kubernetes cluster. Understanding Kubernetes networking. Learning how to use volumes with kubernetes to store data persistently. Securing Kubernetes. Achieving high availability in Kubernetes.

Duration: 24 hours

  • Linux Skills
  • Working knowledge of any cloud platform like AWS, GCP or AZURE
  • Docker / container fundamentals

This course is intended for individuals who want to use Kubernetes Containerization for deploying applications on cloud.

Getting started with Kubernetes
Kubernetes Container Orchestration Architecture
Core Concepts: Objects & Nodes
Google Kubernetes Engine (GKE)
Amazon Elastic Container Services for Kubernetes (EKS)
Azure Kubernetes Service (AKS)

Clustering & Scaling Containers
Creating Kubernetes Cluster in EKS
Deploying Scalable, Stateless Application to EKS

Cluster Network in Kubernetes
Docker Networking Model
Kubernetes Networking Model
Implementing Kubernetes Model

Managing & Storing Data with Kubernetes
Introduction to Volumes
Persistent Volumes
Storage Classes
Dynamic Volumes: Creating on-demand Storage Volumes
Volumes Limits

Planning Production
Using Secrets in Applications
Kubernetes Service
High Availability in Kubernetes
Scaling Kubernetes

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Kubenetes containers are isolated from each other and from the host: they have their own filesystems, they can’t see each others’ processes, and their computational resource usage can be bounded. They are easier to build than VMs, and because they are decoupled from the underlying infrastructure and from the host filesystem, they are portable across clouds and OS distributions.


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