Architecting with Google Kubernetes Engine: Workloads

Course Modality

Instructor-led (classroom)

Course Level

Intermediate

Course Time

21 hours

Course Language

English

Course Overview

In this course, “Architecting with Google Kubernetes Engine: Workloads,” you learn about performing Kubernetes operations; creating and managing deployments; the tools of GKE networking; and how to give your Kubernetes workloads persistent storage.

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning approach that you can adapt to your learning style

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

  • This course is part of a specialization focused on building efficient computing infrastructures using Kubernetes and GKE. The specialization introduces participants to deploying and managing containerized applications on GKE and the other services provided by Google Cloud Platform.
  • Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. The specialization also covers deploying practical solutions including security and access management, resource management, and resource monitoring.

Get In Touch

Course Curriculum

In this module, you’ll become familiar with the structure and layout of the course.

Kubernetes Operations

In this module you will learn about the kubectl command, which is the command line utility used to interact with and manage the resources inside Kubernetes clusters. You’ll learn how to connect it to Google Kubernetes Engine clusters, and use it to create, inspect, interact and delete Pods and other objects within Kubernetes clusters. You’ll also use kubectl to view a Pod’s console output, and sign in interactively to a Pod.

Deployments, Jobs, and Scaling

GKE works with containerized applications: in other words, applications packaged into hardware-independent, isolated user-space instances. In GKE and Kubernetes, these packaged applications are collectively called workloads. In this module you will learn about Deployments and Jobs, two of the main types of workload. You will also learn about the mechanisms that are used to scale the GKE clusters where you run your applications. You’ll learn to control on which Nodes Pods may and may not run. You’ll also explore ways to get software into your cluster. 

Google Kubernetes Engine Networking

In this module, you’ll learn how to create Services to expose applications running within Pods, which allows them to communicate with the outside world. You’ll also learn how to create Ingress resources for HTTP or HTTPS load balancing. You’ll also learn about GKE’s container-native load balancing, which allows you to directly configure Pods as network endpoints with Google Cloud Load Balancing. 

Persistent Data and Storage

In this module you’ll learn about the different types of Kubernetes storage abstractions. You’ll learn about StatefulSets and how to use them to manage ordered deployments of Pods and storage. You’ll also learn how ConfigMaps can save you time during application deployment by decoupling configuration artifacts from container definitions. Finally, you’ll learn how to keep sensitive information safer from accidental exposure using Kubernetes Secrets.

Recommended Exams

Cloud-certificationbadge-CloudDevOpsEngineer

Professional Cloud Developer

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.

Reviews

Get in touch, enquire now!


By submitting this form, I consent to the processing of the personal data that I provide The Data Tech Labs Inc. in accordance with and as described in the Privacy Policy.

© 2020 The Data Tech Labs Inc. All rights reserved.

TDTL-arrow
[glt language="Arabic" label="Arabic" image="yes" text="yes" image_size="24"]
[glt language="English" label="English" image="yes" text="yes" image_size="24"]
[glt language="French" label="French" image="yes" text="yes" image_size="24"]
[glt language="German" label="German" image="yes" text="yes" image_size="24"]
[glt language="Hindi" label="Hindi" image="yes" text="yes" image_size="24"]
[glt language="Marathi" label="Marathi" image="yes" text="yes" image_size="24"]
[glt language="Spanish" label="Spanish" image="yes" text="yes" image_size="24"]

Microsoft Power Platform App Maker

Designing & Implementing Azure AI Solution

Microsoft Azure Administrator

Developing Solutions For Microsoft Azure

Microsoft Azure Architect Design Exam

Implementing Azure Data Solution

Administering Relational Databases On Microsoft Azure