Pearson – Kubernetes on GKE

Pearson – Kubernetes on GKE
English | Tutorial | Size: 708.76 MB


3+ Hours of Video Instruction
Learn the fundamentals you need to use the Google Kubernetes engine.

Kubernetes is the de facto standard for running scalable workloads on-prem and across all major public clouds. Kubernetes on the Google Cloud Platform (GKE) was the first managed version of this container orchestrator on a major public cloud and remains until today one of the most accessible to beginners.

In the Kubernetes on GKE video course, Ernesto Garbarino takes you through the Google Kubernetes Engine and enables you to grasp the fundamentals of running scalable and highly available workloads on GKE and leveraging the ease of use of this platform. The class focuses on the portable capabilities that can be leveraged in other Kubernetes implementations as well.

Throughout the course, Ernesto will cover key Kubernetes fundamentals through a series of demos which include setting up your Google Kubernetes Engine, creating and destroying Kubernetes Clusters, and running containerized workloads, ranging from simple Linux commands, all the way up to a custom fault-tolerant distributed database. By the end of the course, you will gain the confidence to run, scale, and control applications on Kubernetes, and where to explore further.

Topics Covered Include:
• Getting Started with GKE: Provisioning GKE services on the Google Cloud Platform, setting up a Kubernetes cluster and managing using the command line interface.
• Pods: Running and managing containerized workloads, as well as understanding their life cycle.
• High Availability and Service Discovery: Scaling containerized workloads, making them highly-available, and making them accessible on the public internet.
• Jobs and Daemonsets: Running batch jobs, cron jobs, and server-wide ‘daemonized processes.
• Configuration and StatefulSets: Externalizing configuration properties, storing sensitive information (secrets), and deploying persistent, fault-tolerant, distributed workloads.

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