Kubernetes Submissions

📘 DevOps with Kubernetes – Exercise Repository

This repository contains my completed exercises for the DevOps with Kubernetes course. Each exercise links to its own branch and includes screenshots as proof of completion.

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🚀 Quick Navigation – All Exercises

Click an exercise number to jump directly to its section.

Chapter	Exercises
Chapter 1	1.1 · 1.2 · 1.3 · 1.4 · 1.5 · 1.6 · 1.7 · 1.8 · 1.9 · 1.10 · 1.11 · 1.12 · 1.13
Chapter 2	2.1 · 2.2 · 2.3 · 2.4 · 2.5 · 2.6 · 2.7 · 2.8 · 2.9 · 2.10
Chapter 3	3.1 · 3.2 · 3.3 · 3.4 · 3.5 · 3.6 · 3.7 · 3.8 · 3.9 · 3.10 · 3.11 · 3.12
Chapter 4	4.1 · 4.2 · 4.3 · 4.4 · 4.5 · 4.6 · 4.7 · 4.8 · 4.9 · 4.10
Chapter 5	5.1 · 5.6 · 5.7

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📊 Progress Overview

Chapter	Exercises Completed
Chapter 1	1.1 – 1.13
Chapter 2	2.1 – 2.10
Chapter 3	3.1 – 3.12
Chapter 4	4.1 – 4.10
Chapter 5	5.1, 5.6, 5.7

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🧩 Chapter 1 – Getting Started

Exercise 1.1

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Exercise 1.2

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Exercise 1.3

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Exercise 1.4

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Exercise 1.5

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Exercise 1.6

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Exercise 1.7

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Exercise 1.8

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Exercise 1.9

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Exercise 1.10

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Exercise 1.11

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Exercise 1.12

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Exercise 1.13

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🧩 Chapter 2 – Kubernetes Basics

Exercise 2.1

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Exercise 2.2

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Exercise 2.3

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Exercise 2.4

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Exercise 2.5

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Exercise 2.6

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Exercise 2.7

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Exercise 2.8

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Exercise 2.9

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Exercise 2.10

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🧩 Chapter 3 – More Building Blocks

Exercise 3.1

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Exercise 3.2

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Exercise 3.3

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Exercise 3.4

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Exercise 3.5

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Exercise 3.6

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Exercise 3.7

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Exercise 3.8

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Exercise 3.9

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Exercise 3.10

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Exercise 3.11

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Exercise 3.12

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☁️ Chapter 4 – To the Cloud

Exercise 4.1

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Exercise 4.2

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Exercise 4.3

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Exercise 4.4

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Exercise 4.5

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Exercise 4.6

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Exercise 4.7

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Exercise 4.8

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Exercise 4.9

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Exercise 4.10

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🧩 Chapter 5 – Advanced Topics

Exercise 5.1

• Branch: 5.1
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Exercise 5.6

• Branch: 5.6
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Notes:

• Knative Service deployed on k3d; revision reached Running state.
• Due to corporate TLS interception, Kourier ingress manifest could not be fetched.
• Service verified via port-forward to the revision pod, confirming build and request handling.

Exercise 5.7

• Branch: 5.7

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📝 Notes – DBaaS (Cloud SQL) vs DIY PostgreSQL on GKE (PVC)

1. Initialization & Setup

DBaaS (Google Cloud SQL)

• ✅ Fast to initialize (minutes via UI / CLI / Terraform)
• ✅ No need to manage storage, replication, or failover
• ❌ Requires networking setup (private IP, Cloud SQL Proxy / connector)

DIY (PostgreSQL on GKE with PVCs)

• ❌ More setup work (StatefulSets, PVCs, StorageClasses)
• ❌ High availability and failover must be designed manually
• ✅ Full control over database configuration and deployment

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2. Maintenance & Operations

DBaaS

• ✅ Automatic updates, patching, and security fixes
• ✅ Managed high availability and failover
• ❌ Limited control over internal PostgreSQL configuration
• ❌ Vendor lock-in to Google Cloud

DIY

• ❌ Manual upgrades, monitoring, and failure recovery
• ❌ Requires strong Kubernetes and database expertise
• ✅ Full control over PostgreSQL version and tuning
• ✅ Easier portability across environments

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3. Backups & Recovery

DBaaS

• ✅ Automated daily backups
• ✅ Point-in-time recovery supported
• ✅ Simple restore via UI or CLI
• ❌ Limited customization of backup strategy

DIY

• ❌ Backups must be implemented manually (pg_dump, cronjobs, operators)
• ❌ Restore process is more complex and error-prone
• ✅ Full control over backup format, schedule, and storage location

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4. Scalability & Reliability

DBaaS

DIY

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5. Costs

DBaaS

• ❌ Higher service cost due to managed features
• ❌ Pay for HA, backups, and availability even when underutilized
• ✅ Lower operational cost (less maintenance effort)

DIY

• ✅ Potentially lower infrastructure cost
• ❌ Higher operational and engineering effort
• ❌ Risk of downtime increases indirect costs

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6. When to Choose Which

Choose DBaaS when:

• Reliability and data safety are top priorities
• Operational effort must be minimized
• Database expertise is limited

Choose DIY when:

• Full control and portability are required
• You want to learn or customize PostgreSQL deeply
• Cost optimization is critical and team expertise is sufficient

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Conclusion

DBaaS favors simplicity, reliability, and low operational overhead at a higher service cost, while DIY PostgreSQL on GKE offers greater control and flexibility but requires significantly more maintenance and operational expertise.