Deploying Containerized Workloads Using Google Cloud Kubernetes Engine

This course deals with the Google Kubernetes Engine, the most robust and seamless way to run containerized workloads on the GCP. Cluster creation, the use of volume storage abstractions, and ingress and service objects are all covered in this course.
Course info
Rating
(24)
Level
Beginner
Updated
Jan 11, 2019
Duration
2h 51m
Table of contents
Course Overview
Introducing Google Kubernetes Engine (GKE)
Creating and Administering GKE Clusters
Deploying Containerized Workloads to GKE Clusters
Monitoring GKE Clusters Using Stackdriver
Description
Course info
Rating
(24)
Level
Beginner
Updated
Jan 11, 2019
Duration
2h 51m
Description

Running Kubernetes clusters on the cloud involves working with a variety of technologies, including Docker, Kubernetes, and GCE Compute Engine Virtual Machine instances. This can sometimes get quite involved. In this course, Deploying Containerized Workloads Using Google Cloud Kubernetes Engine, you will learn how to deploy and configure clusters of VM instances running your Docker containers on the Google Cloud Platform using the Google Kubernetes Service. First, you will learn where GKE fits relative to other GCP compute options such as GCE VMs, App Engine, and Cloud Functions. You will understand fundamental building blocks in Kubernetes, such as pods, nodes and node pools, and how these relate to the fundamental building blocks of Docker, namely containers. Pods, ReplicaSets, and Deployments are core Kubernetes concepts, and you will understand each of these in detail. Next, you will discover how to create, manage, and scale clusters using the Horizontal Pod Autoscaler (HPA). You will also learn about StatefulSets and DaemonSets on the GKE. Finally, you will explore how to share states using volume abstractions, and field user requests using service and ingress objects. You will see how custom Docker images are built and placed in the Google Container Registry, and learn a new and advanced feature, binary authorization. When you’re finished with this course, you will have the skills and knowledge of the Google Kubernetes Engine needed to construct scalable clusters running Docker containers on the GCP.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Deploying Containerized Workloads Using Google Cloud Kubernetes Engine. A little about myself. I have a masters in electrical engineering from Stanford, and have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. In this course, you will gain the ability to deploy and configure clusters of VM instances running your Docker containers on the Google Cloud platform using the Google Kubernetes engine. First, you'll learn where the GKE fits relative to other GCP compute options, such as VMs, app engine, and cloud functions. You will understand the fundamental building blocks in Kubernetes such as pods, nodes, node pools, and how these relate to the fundamental building blocks of Docker, namely containers. Kubernetes has several powerful abstractions such as replica sets, which add horizontal scaling to pods and deployments which provide deployment and rollback functionality to replica sets. These are code Kubernetes concepts and you'll understand each of these in detail. Next, you will discover how to create, manage, and scale clusters. This involves the use of something known as the horizontal pod autoscaler HPE, which is a different scaling mechanism than that used with GCP VM instance coops. Finally, you will explore how to share state using volume abstractions and field user requests using servers and ingress objects. You'll see how custom Docker images are built and placed in the Google container registry providing the seamless integration between GCP and Kubernetes. You will also learn a new and advanced feature, binary authorization, which can be used to ensure that only signed verified container images are deployed to your cluster. When you are finished with this course, you will have the skills and knowledge of the Google Kubernetes engine needed to construct scalable clusters running Docker containers on the GCP.