Running Kubernetes on Google Cloud: A Practical Guide to GKE with DevOps Workflows

Overview

This course is built for learners who want to understand Kubernetes on Google Cloud through extensive, real-world practice. Using Google Kubernetes Engine (GKE) as the foundation, it progresses from Kubernetes basics to advanced production scenarios—covering storage, networking, ingress, autoscaling, and DevOps automation.
Rather than focusing on isolated features, the course emphasizes how Kubernetes components and Google Cloud services work together in realistic deployments.

Course Snapshot

• Instructor: Kalyan Reddy
• Cloud platform: Google Cloud
• Container service: Google Kubernetes Engine
• Last updated: —
• Total learning time: ~26 hours
• Level: Beginner to advanced
• Language support: English, German (Auto)
• Certification: Included
• Access type: Full lifetime (mobile & TV supported)

What This Course Focuses On

The course is structured around learning Kubernetes by implementing it repeatedly across many scenarios. Learners build both standard and autopilot GKE clusters, deploy applications end to end, and integrate core Google Cloud services to mirror real production environments.

Key focus areas include:

• Creating and managing GKE clusters in different network models
• Deploying applications with storage, load balancing, and ingress
• Implementing DevOps CI/CD pipelines on Google Cloud
• Operating Kubernetes at scale with security and autoscaling features

Skills & Concepts You’ll Work With

Kubernetes & GKE Foundations

• Core Kubernetes concepts: pods, services, deployments, and namespaces
• Creating GKE Standard and Autopilot clusters
• Working with public and private cluster networking

Storage, Networking & Ingress

• Persistent storage using Google Persistent Disks and Filestore
• Application deployments backed by Cloud SQL
• Kubernetes ingress on GKE with multiple routing strategies
• Managed SSL, pre-shared certificates, and self-signed certificates
• Advanced ingress features including CDN, Cloud Armor, and internal load balancers

Identity, Security & DNS

• Workload Identity for secure service access
• External DNS automation with Google Cloud DNS
• Identity-Aware Proxy (IAP) integration
• Probes, resource requests, limits, quotas, and namespace controls

DevOps & CI/CD on Google Cloud

• Building CI pipelines with Cloud Build
• Implementing CD workflows with Cloud Source services
• Image management using Google Artifact Registry

Scaling & Reliability

• Horizontal Pod Autoscaler (HPA)
• GKE Cluster Autoscaler
• Readiness, liveness, and startup probes for resilient workloads

Certified Kubernetes Administrator (CKA) with Practice Tests

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Who This Course Is Best Suited For

• Beginners starting with Kubernetes on Google Cloud
• Cloud engineers working with GKE-based platforms
• DevOps professionals building CI/CD pipelines for Kubernetes
• Engineers transitioning from basic containers to orchestration
• Learners who prefer demo-driven, hands-on training

Common Questions Learners Ask

Do I need prior Kubernetes experience?
No. The course begins with fundamentals and gradually advances to complex scenarios.

Is a Google Cloud account required?
Yes. Hands-on demos require an active Google Cloud account.

Does the course focus on real production use cases?
Yes. Topics such as ingress, autoscaling, identity, and CI/CD reflect production environments.

Are DevOps pipelines included?
Yes. CI/CD is implemented using native Google Cloud tools.

Is this suitable for certification preparation?
While not exam-specific, the skills align closely with real-world GKE operations.

Practical Value

What sets this course apart is its demo-heavy approach. By implementing the same Kubernetes concepts across many variations, learners develop a practical understanding of how GKE behaves in real environments—especially around ingress, networking, and scaling, which are often challenging in production.

Final Thoughts

If you want to learn Kubernetes on Google Cloud through extensive hands-on practice, this course offers a structured and realistic path. Its combination of GKE fundamentals, advanced ingress scenarios, and DevOps automation makes it particularly useful for engineers working with containerized workloads on Google Cloud.