Architecting with Google Kubernetes Engine: Workloads

  • 4.7
Approx. 16 hours to complete

Course Summary

Learn how to deploy workloads on Google Kubernetes Engine (GKE) with this comprehensive course. Gain hands-on experience in creating, deploying, and managing containerized applications on GKE.

Key Learning Points

  • Learn to deploy workloads on GKE using containers
  • Gain hands-on experience in managing containerized applications
  • Understand the best practices for deploying workloads on GKE

Job Positions & Salaries of people who have taken this course might have

    • USA: $97,000
    • India: ₹1,200,000
    • Spain: €37,000
    • USA: $97,000
    • India: ₹1,200,000
    • Spain: €37,000

    • USA: $115,000
    • India: ₹1,500,000
    • Spain: €41,000
    • USA: $97,000
    • India: ₹1,200,000
    • Spain: €37,000

    • USA: $115,000
    • India: ₹1,500,000
    • Spain: €41,000

    • USA: $120,000
    • India: ₹1,800,000
    • Spain: €50,000

Related Topics for further study


Learning Outcomes

  • Create and manage containerized applications on GKE
  • Deploy and scale workloads on GKE
  • Understand the best practices for deploying workloads on GKE

Prerequisites or good to have knowledge before taking this course

  • Familiarity with containerization and Kubernetes
  • Basic knowledge of cloud computing

Course Difficulty Level

Intermediate

Course Format

  • Online self-paced
  • Hands-on labs
  • Video lectures

Similar Courses

  • Architecting with Google Kubernetes Engine
  • Google Cloud Platform Fundamentals for AWS Professionals

Related Education Paths


Related Books

Description

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.

Knowledge

  • Understand the role of the kubectl command
  • Create and use deployments, and create run jobs and cron jobs
  • Create services and use load balancers to expose services to external clients
  • Understand and work with different Kubernetes storage abstractions

Outline

  • Course Introduction
  • Course Introduction
  • Getting Started with Google Cloud Platform and Qwiklabs
  • Welcome and Getting Started Guide!
  • How to Send Feedback
  • Kubernetes Operations
  • Introduction
  • The kubectl Command
  • Introspection
  • Lab Intro
  • Lab Intro
  • Summary
  • The kubectl Command
  • Introspection
  • Kubernetes Operations
  • Deployments, Jobs, and Scaling
  • Introduction
  • Deployments
  • Ways to Create Deployments
  • Services and Scaling
  • Updating Deployments
  • Rolling Updates
  • Blue-Green Deployments
  • Canary Deployments
  • Managing Deployments
  • Lab Intro
  • Jobs and CronJobs
  • Parallel Jobs
  • CronJobs
  • Lab Intro
  • Cluster Scaling
  • Downscaling
  • Node Pools
  • Controlling Pod Placement
  • Affinity and Anti-Affinity
  • Pod Placement Example
  • Taints and Tolerations
  • Getting software into your cluster
  • Lab Intro
  • Summary
  • Deployments
  • Updating Deployments
  • Jobs
  • Cluster Scaling
  • Controlling Pod Placement
  • Deployments, Jobs, and Scaling
  • Google Kubernetes Engine (GKE) Networking
  • Introduction
  • Pod Networking
  • Services
  • Finding Services
  • Service Types and Load Balancers
  • How Load Balancers work
  • Ingress Resource
  • Container-Native Load Balancing
  • Network Security
  • Lab Intro
  • Lab Intro
  • Summary
  • Table: Load balancing objects in GKE
  • Pod Networking
  • Services
  • Service Types
  • Ingress
  • Network Security
  • Google Kubernetes Engine Networking
  • Persistent Data and Storage
  • Introduction
  • Volumes
  • Volume types
  • Volume types 2
  • The PersistentVolume abstraction
  • More on PersistentVolumes
  • StatefulSets
  • Lab Intro
  • ConfigMaps
  • Secrets
  • Lab Intro
  • Summary
  • Next steps
  • Volumes
  • StatefulSets
  • ConfigMaps
  • Secrets
  • Persistent Data and Storage

Summary of User Reviews

Discover how to deploy workloads on Google Kubernetes Engine (GKE) with this comprehensive course from Coursera. Students love the in-depth coverage of GKE and its features, as well as the hands-on experience gained through labs and exercises.

Key Aspect Users Liked About This Course

The hands-on experience gained through labs and exercises

Pros from User Reviews

  • In-depth coverage of GKE and its features
  • Great for beginners and experienced users alike
  • Clear explanations and helpful examples
  • Great value for the price
  • Excellent guidance from instructors

Cons from User Reviews

  • Some students found the labs to be challenging
  • Course can be quite technical and may require prior knowledge
  • Some students felt the pacing was too slow
  • Not ideal for those looking for a quick overview of GKE
  • Limited interaction with instructors and other students
English
Available now
Approx. 16 hours to complete
Google Cloud Training
Google Cloud
Coursera

Instructor

Google Cloud Training

  • 4.7 Raiting
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