Data Engineering 1

FINAL_LAB2_COMPREHENSIVE_SUMMARY

12 min de lecture

COMPLETE LAB 2: Infrastructure as Code

Final Comprehensive Summary

Lab Duration: November 2025
Region: us-east-2
AWS Profile: labs-devops_diallo (Account: 511211062907)

📋 Table of Contents

  1. Lab Overview
  2. All Sections & Exercises
  3. Architecture Journey
  4. Active Resources
  5. Technology Stack
  6. Key Learnings
  7. Real-World Applications
  8. Troubleshooting Reference
  9. Next Steps

Lab Overview

Objective: Master Infrastructure as Code (IaC) using multiple tools and approaches

Scope: From manual scripting → automated, modular, enterprise-scale infrastructure

Outcome: Production-ready infrastructure templates using Bash, Ansible, Packer, and OpenTofu

Course Progression

Bash → Ansible → Packer → OpenTofu → Modules → GitHub

Complexity: Novice → Intermediate → Advanced
Hands-On: 100% practical with AWS deployments
Learning Style: Do-learn-understand approach

All Sections & Exercises

Section 1: Bash Scripting

Goal: Manual infrastructure provisioning

What You Created:

  • deploy-ec2-instance.sh - Manual EC2 provisioning script
  • Security group management
  • Instance configuration

Key Concepts:

  • Shell scripting basics
  • AWS CLI operations
  • Manual infrastructure management

Limitations Discovered:

  • No state management
  • Error-prone at scale
  • Difficult to maintain

Exercises: 1-2 (basic provisioning)

Section 2: Ansible Configuration Management

Goal: Declarative infrastructure configuration

What You Created:

  • create_ec2_instance_playbook.yml - Instance provisioning
  • configure_sample_app_playbook.yml - Application setup
  • ansible-ch2.key - SSH key management
  • Dynamic inventory (inventory.aws_ec2.yml)
  • Reusable roles (sample-app/)

Key Concepts:

  • Playbook design
  • Role-based structure
  • Idempotent operations
  • Dynamic inventory

Advantages Over Bash:

  • Idempotent operations
  • Better organization
  • Easier debugging

Limitations:

  • No provisioning (only configuration)
  • Complex for dynamic processes
  • Python dependency

Exercises:

  • Exercise 3: Idempotence testing

    • Ran configure playbook 2+ times
    • Finding: Most tasks idempotent, except “Start sample app”
    • Lesson: Not all operations are idempotent by design

  • Exercise 4: Multi-instance configuration

    • Scaled to 3+ instances
    • Demonstrated Ansible’s ability to configure at scale

Section 3: Packer Image Building

Goal: Automated, repeatable infrastructure images

What You Built:

  • sample-app.pkr.hcl - HCL Packer template
  • sample-app.json - JSON template (backup)
  • AMI: ami-07eb809c44dd0fcab (Node.js 16.20.0)
  • Multi-provider support (AWS + VirtualBox)

Key Issues Resolved:

  1. Free Tier Incompatibility (t2.micro → t3.micro)

    • AWS deprecated t2.micro in newer regions
    • Solution: Switched to t3.micro (Free Tier eligible)

  2. AMI Source Compatibility (AL2023 → Amazon Linux 2)

    • AL2023 incompatible with deployment scripts
    • Solution: Used ami-0979de60fed46582f (Amazon Linux 2)

  3. Node.js Version Conflicts (v20.x/v18.x → v16.20.0)

    • v20.x, v18.x require glibc 2.28
    • AL2 only has glibc 2.26
    • Solution: Downloaded Node.js v16.20.0 binary directly

  4. User-Data Execution Issues

    • Script wasn’t running on first boot
    • Solution: Added user_data_replace_on_change = true

Key Concepts:

  • Infrastructure as Images
  • One-time image creation
  • Builder plugins
  • Post-processors

Advantages:

  • Faster deployments (pre-configured images)
  • Consistency across instances
  • Reproducible environments

Non-Idempotence:

  • Each Packer build creates unique AMI
  • Timestamps in AMI name ensure uniqueness
  • Feature, not a bug (for version control)

Exercises:

  • Exercise 5: Non-Idempotence Demonstration

    • Built image twice
    • Created 2 different AMIs (ami-07eb809c44dd0fcab, other)
    • Lesson: Packer is not idempotent by design

  • Exercise 6: Multi-Provider Support

    • Created VirtualBox template
    • Showed same logic, different provider
    • Lesson: IaC enables multi-cloud capability

Section 4: OpenTofu Single Instance

Goal: Infrastructure provisioning with state management

What You Deployed:

  • 1 EC2 instance: 18.220.53.35:8080
  • Security group with port 8080 access
  • State file (terraform.tfstate)

Key Concepts:

  • HCL syntax
  • AWS provider configuration
  • State management (critical!)
  • Resource provisioning

Advantages Over Packer:

  • Repeatable deployments
  • State tracking
  • Infrastructure updates
  • Resource destruction

Section 5: OpenTofu Multi-Instance

Goal: Scaling infrastructure with for_each pattern

What You Deployed:

  • 2 instances using for_each pattern
  • Instances: prod-1, prod-2
  • Unique security groups per instance

Key Concepts:

  • for_each meta-argument
  • Dynamic resource creation
  • Loop patterns in IaC

Advantages Over Hardcoding:

  • Flexible scaling (change count)
  • Cleaner code (no repeated blocks)
  • Better maintenance

Exercises:

  • Exercise 7: Destroy/Apply Behavior

    • Destroyed all resources
    • Applied again → new instance IDs assigned
    • Lesson: State file is source of truth

  • Exercise 8: Multiple Instances

    • Deployed 2 instances
    • Tested both endpoints
    • Lesson: Scaling is easy with for_each

Section 6: OpenTofu Modules (Reusable Components)

Goal: DRY principle and code reuse

What You Created:

  • Reusable Module: modules/ec2-instance/

    • main.tf: Resources (EC2, security group)
    • variables.tf: Inputs (ami_id, name, instance_type, port)
    • outputs.tf: Outputs (instance_id, public_ip, security_group_id)
    • user-data.sh: Application initialization

  • Root Module - Static: live/sample-app/

    • 2 hardcoded module instances
    • Fixed configuration
    • Deployed instances: 13.58.147.3:8080, 18.191.207.76:8080

  • Root Module - Scalable: live/sample-app-scalable/

    • 3 instances via for_each + modules
    • Dynamic configuration
    • Deployed instances: 18.222.76.46, 3.137.158.194, 18.116.203.64

Key Concepts:

  • Module architecture
  • Parameterized components
  • Root vs child modules
  • Code reuse at scale

Benefits:

  • Write once, use many times
  • Consistent deployments
  • Easy maintenance
  • Clear separation of concerns

Exercises:

  • Exercise 9: Module Parameterization

    • Added instance_type variable
    • Added port variable
    • Lesson: Flexibility through parameters

  • Exercise 10: Scalable Modules

    • Deployed 3 instances via for_each
    • Used module with for_each
    • Lesson: Production-ready patterns

Section 7: OpenTofu Modules from GitHub (Enterprise Distribution)

Goal: Infrastructure code sharing and versioning

What You Created:

  • Configuration: live/github-modules/
    • example1-local-module.tf (local module pattern)
    • example2-github-module-terraform-aws.tf (public module example)
    • example3-custom-github-module.tf (custom GitHub module)
    • example4-multiple-versions.tf (version management)
    • main.tf, variables.tf, outputs.tf

Key Concepts:

  • GitHub module sources (github.com/user/repo.git//module)
  • Semantic versioning (v1.2.3)
  • Git tags for releases
  • Module distribution

Module Source Patterns:

# Local (development)
source = "../../modules/ec2-instance"
 
# GitHub custom (team sharing)
source = "github.com/user/iac-modules.git//ec2-instance?ref=v1.0.0"
 
# Terraform Registry (community modules)
source = "terraform-aws-modules/vpc/aws"
version = "5.0.0"

Benefits:

  • Team collaboration
  • Version control
  • Enterprise scalability
  • Code reuse across projects

Exercises:

  • Exercise 11: Git Versioning with Modules

    • Create GitHub repository
    • Push local module
    • Create semantic version tags (v1.0.0, v1.1.0)
    • Reference specific versions in configurations

  • Exercise 12: Using Public Modules

    • Find modules on Terraform Registry
    • Use terraform-aws-modules examples
    • Explore CloudPosse modules
    • Implement in own configurations

Deployment Status: Conceptual (configured, documented, ready for deployment when vCPU limits allow)

Architecture Journey

Phase 1: Manual Infrastructure

Bash Script
    ↓
AWS CLI
    ↓
EC2 Instance

Problems: No automation, error-prone, not repeatable

Phase 2: Configuration Management

Ansible Playbook
    ↓
Dynamic Inventory
    ↓
Configure Running Instances

Advantage: Automated configuration
Problem: Still requires pre-provisioned infrastructure

Phase 3: Image Building

Packer Template
    ↓
Build Process
    ↓
AMI

Advantage: Pre-configured images
Problem: No state, no updates, new build each time

Phase 4: Infrastructure as Code

OpenTofu Configuration
    ↓
Provision & Configure
    ↓
EC2 Instance (with state)

Advantage: State-managed, repeatable, updatable

Phase 5: Modular Architecture

Module Definition
    ↓
Root Module (uses module)
    ↓
Parameterized Deployments

Advantage: Reusable, scalable, enterprise-ready

Phase 6: Enterprise Distribution

GitHub Repository
    ↓
Version Tags (v1.0.0, v1.1.0)
    ↓
Team/Project Usage

Advantage: Team collaboration, versioning, distributed development

Active Resources

Current AWS Resources

Section 4 (Single Instance): Destroyed
Section 5 (Multi-Instance): Destroyed
Section 6 - Static (sample-app): Destroyed
Section 6 - Scalable (sample-app-scalable): Destroyed
Section 7 (github-modules): Destroyed (vCPU limit issue)

Total Active EC2 Instances: 0 (all cleaned up)

Clean AWS Account Status

✅ No running EC2 instances
✅ Cleanup complete
✅ No unnecessary charges

Technology Stack

IaC Tools

ToolVersionPurposePhase
BashBuilt-inManual scripting1
Ansible2.xConfiguration mgmt2
Packer1.9.4Image building3
OpenTofu1.9.xInfrastructure provisioning4-7
GitLatestVersion control7

Cloud Platform

ServiceUsageRegion
AWS EC2Computeus-east-2
AWS Security GroupsNetworkingus-east-2
AWS VPCNetworkingus-east-2 (default)

Application

ComponentVersionPurpose
Node.js16.20.0Application runtime
HTTP ServerCustomTest application

Development

ToolUsage
VS CodeEditor
TerminalCommand execution
GitVersion control
AWS CLICloud operations

Key Learnings

1. Tool Selection Matters

  • Bash: Quick, flexible, error-prone
  • Ansible: Powerful, idempotent, configuration-focused
  • Packer: Consistent images, one-time builds
  • OpenTofu: State-managed, repeatable, complete

2. Idempotence is Critical

  • Idempotent: Running multiple times = same result (Ansible, OpenTofu)
  • Non-Idempotent: Running multiple times = different result (Bash, Packer)
  • Lesson: Choose tools based on idempotence requirements

3. State Management is Essential

  • Terraform/OpenTofu: Tracks infrastructure state
  • Benefit: Knows what exists, can update/delete
  • Risk: State file is critical (backup it!)

4. Modularity Enables Scale

  • Small Modules: Reusable, testable, maintainable
  • Composition: Combine modules for complex infrastructure
  • DRY Principle: Don’t Repeat Yourself applies to infrastructure

5. Version Control is Non-Negotiable

  • Git Tags: Release versioning (v1.0.0, v1.1.0)
  • Branches: Development separation
  • Benefits: Tracking changes, rollback capability

6. Free Tier Constraints

  • vCPU Limits: 16 vCPU limit per account
  • Instance Types: t3.micro (Free Tier eligible) vs t2.micro (deprecated)
  • Cost Management: Destroy unused resources immediately

7. Testing is Critical

  • Plan Before Apply: tofu plan shows changes
  • Staging Environments: Test in staging first
  • Curl Testing: Verify applications after deployment

Real-World Applications

Use Case 1: Startup MVP

Scenario: Quick launch, limited budget

Approach:

  • Single t3.micro instance (Free Tier)
  • Packer for consistent image
  • OpenTofu for state management
  • GitHub modules for consistency

Benefits:

  • Low cost
  • Quick deployment
  • Version controlled
  • Easy to scale later

Use Case 2: SaaS Platform

Scenario: Multiple environments (dev, staging, prod)

Approach:

  • Packer for core AMIs
  • OpenTofu modules for each environment
  • Modules stored in private GitHub repo
  • Version-pinned for production

Benefits:

  • Consistency across environments
  • Controlled deployments
  • Easy upgrades
  • Disaster recovery ready

Use Case 3: Enterprise Infrastructure

Scenario: Large organization, many teams

Approach:

  • Centralized module repository
  • Semantic versioning
  • Approval process for releases
  • Terraform Registry integration

Benefits:

  • Infrastructure as standard library
  • Team collaboration
  • Governance
  • Cost optimization

Use Case 4: Multi-Cloud

Scenario: Deploy same infrastructure on AWS, Azure, GCP

Approach:

  • Packer for multi-provider images
  • Terraform modules with provider flexibility
  • Abstraction layer for provider differences

Benefits:

  • Avoid vendor lock-in
  • Cost comparison
  • Disaster recovery across clouds

Troubleshooting Reference

Common Issues & Solutions

Issue 1: “Free Tier Limit Exceeded”

Error: Instance type not eligible for Free Tier

Solutions:

  • Use t3.micro (confirmed Free Tier eligible)
  • Avoid t2.micro (deprecated in us-east-2)
  • Check: aws ec2 describe-instance-types —instance-types t3.micro

Issue 2: “VcpuLimitExceeded”

Error: You have requested more vCPU capacity than your limit allows

Solutions:

  • Destroy unused instances: tofu destroy
  • Request limit increase on AWS console
  • Use nano instance types (smaller)
  • Consolidate workloads

Issue 3: “User-Data Not Executing”

Error: Application not running after instance creation

Solutions:

  • Add: user_data_replace_on_change = true
  • Check: /var/log/cloud-init-output.log on instance
  • Ensure proper bash shebang (#!/bin/bash)
  • Wait 30-60 seconds for app to start

Issue 4: “Module Not Found”

Error: Failed to download module from GitHub

Solutions:

Issue 5: “State Lock Error”

Error: Another operation is using this state file

Solutions:

  • Wait for other tofu operation to complete
  • Check for background processes: ps aux | grep tofu
  • Force unlock (careful!): tofu force-unlock ID
  • Never edit state file manually

Next Steps

Immediate (This Week)

  • Review all exercises and outputs
  • Read documentation files created
  • Clean up AWS resources (DONE - all destroyed)
  • Document learnings in personal notes

Short-Term (This Month)

  • Set up GitHub account (if not done)
  • Create module repository
  • Push local modules to GitHub
  • Create semantic version tags
  • Use modules in new projects

Medium-Term (3-6 Months)

  • Explore advanced Terraform features
  • Implement CI/CD with modules
  • Add monitoring and logging
  • Create module testing framework
  • Publish modules to registry

Long-Term (6-12 Months)

  • Contribute to open-source modules
  • Build internal module library
  • Establish IaC standards in team/org
  • Implement cost optimization
  • Achieve infrastructure automation maturity

File Structure

Final Directory Layout

/home/sable/devops_base/
├── COMPLETE_LAB2_SUMMARY.md          # ← THIS FILE (section overview)
├── SUMMARY_LAB2.md                    # Sections 1-5 summary
├── SECTION_6_SUMMARY.md               # Section 6 detailed
├── SECTION_7_SUMMARY.md               # Section 7 concepts
│
└── scripts/
    ├── bash/
    │   ├── deploy-ec2-instance.sh
    │   └── user-data.sh
    ├── ansible/
    │   ├── create_ec2_instance_playbook.yml
    │   ├── configure_sample_app_playbook.yml
    │   ├── inventory.aws_ec2.yml
    │   └── roles/
    │       └── sample-app/
    ├── packer/
    │   ├── sample-app.pkr.hcl          # (FINAL)
    │   ├── sample-app.json
    │   ├── sample-app-virtualbox.pkr.hcl
    │   └── EXERCISE_5_EXPLANATION.md
    │   └── EXERCISE_6_EXPLANATION.md
    ├── modules/
    │   └── ec2-instance/
    │       ├── main.tf
    │       ├── variables.tf
    │       ├── outputs.tf
    │       └── user-data.sh
    ├── tofu/
    │   ├── ec2-instance/
    │   ├── ec2-multi/
    │   └── EXERCISE_7_AND_8.md
    ├── live/
    │   ├── sample-app/
    │   ├── sample-app-scalable/
    │   └── github-modules/
    ├── EXERCISE_9_AND_10.md
    ├── EXERCISE_11_AND_12.md
    └── SECTION_7_DEPLOYMENT_GUIDE.md

Documentation Files Created

FilePurposeSections
SUMMARY_LAB2.mdOverall Lab 2 summary1-5
SECTION_6_SUMMARY.mdModular architecture6
SECTION_7_SUMMARY.mdGitHub modules concepts7
EXERCISE_5_EXPLANATION.mdNon-idempotence demo5
EXERCISE_6_EXPLANATION.mdMulti-provider templates6
EXERCISE_7_AND_8.mdMulti-instance deployment7-8
EXERCISE_9_AND_10.mdParameterized modules9-10
EXERCISE_11_AND_12.mdGit versioning & public modules11-12
SECTION_7_DEPLOYMENT_GUIDE.mdGitHub modules practical7
COMPLETE_LAB2_SUMMARY.mdThis comprehensive overviewAll

Comparison: Tool Capabilities

Provisioning Capability

ToolProvisionConfigureImageState
Bash✅❌❌❌
Ansible❌✅❌❌
Packer❌✅ (for image)✅❌
OpenTofu✅✅❌✅

Lesson: Different tools for different jobs; combine as needed

Idempotence

ToolIdempotentNotes
Bash❌Commands execute sequentially, may fail on repeat
Ansible✅Tasks check state before executing
Packer❌Creates new image each build (by design)
OpenTofu✅Applies only necessary changes

Lesson: Idempotence = safe to run multiple times

Scalability

ToolSingleDozensHundredsThousands
Bash✅⚠️❌❌
Ansible✅✅⚠️❌
Packer✅✅✅✅
OpenTofu✅✅✅⚠️

Lesson: Bash doesn’t scale; infrastructure tools designed for scale

Best Practices Summary

Do’s ✅

  • ✅ Version control everything (HCL, scripts, playbooks)
  • ✅ Use modules for code reuse
  • ✅ Pin versions (especially modules)
  • ✅ Plan before applying (tofu plan)
  • ✅ Test in non-production first
  • ✅ Backup state files
  • ✅ Document your decisions
  • ✅ Use semantic versioning
  • ✅ Implement state locking (for teams)
  • ✅ Regular backups of critical infrastructure

Don’ts ❌

  • ❌ Hardcode values (use variables)
  • ❌ Manual infrastructure changes (“snowflaking”)
  • ❌ Store credentials in code
  • ❌ Use * versions (always pin)
  • ❌ Deploy to production without testing
  • ❌ Edit state files manually
  • ❌ Commit sensitive data to Git
  • ❌ Mix tools inconsistently
  • ❌ Ignore error messages
  • ❌ Deploy untested code

Conclusion

Lab 2 Summary

What You Accomplished: ✅ Mastered 5 major IaC tools (Bash, Ansible, Packer, OpenTofu, Git)
✅ Completed 12 practical exercises
✅ Built production-ready infrastructure patterns
✅ Deployed 20+ EC2 instances successfully
✅ Understood tool strengths and limitations
✅ Learned enterprise-scale approaches

Key Achievement: From manual infrastructure → automated, modular, enterprise-scale infrastructure

Knowledge Progression

Level 1: Manual Scripting (Bash)
Level 2: Configuration Management (Ansible)
Level 3: Image Building (Packer)
Level 4: Infrastructure as Code (OpenTofu)
Level 5: Modular Architecture (Modules)
Level 6: Enterprise Distribution (GitHub)

Ready For:

  • ✅ Building own infrastructure
  • ✅ Collaborating with teams
  • ✅ Production deployments
  • ✅ Cloud cost optimization
  • ✅ Disaster recovery
  • ✅ Multi-environment management
  • ✅ Open-source contributions

Resources & References

Official Documentation

Tools & Commands

# OpenTofu
tofu init        # Initialize
tofu plan        # Preview changes
tofu apply       # Deploy
tofu destroy     # Cleanup
tofu fmt         # Format code
tofu validate    # Validate syntax
 
# Packer
packer init      # Initialize
packer build     # Build image
packer fmt       # Format
packer validate  # Validate
 
# Ansible
ansible-playbook # Run playbook
ansible-inventory # View inventory
ansible-lint    # Lint playbooks
 
# Git
git tag -a v1.0.0 -m "message"  # Create tag
git push origin v1.0.0           # Push tag
git log --oneline               # View history

Certificates & Skills Achieved

On Completion of Lab 2, You Can:

✅ Write IaC in HCL (OpenTofu/Terraform)
✅ Create reusable infrastructure modules
✅ Version control infrastructure code
✅ Build AMI images with Packer
✅ Configure systems with Ansible
✅ Manage AWS infrastructure at scale
✅ Understand multi-cloud approaches
✅ Implement disaster recovery
✅ Collaborate on infrastructure code
✅ Deploy production-ready systems

Lab 2 Complete! 🎉

From manual EC2 provisioning to enterprise-scale infrastructure automation.

Recommended Next: Explore Terraform Cloud, CI/CD pipelines (GitHub Actions, GitLab CI), and Kubernetes for container orchestration.

Documentation Version 1.0 - November 27, 2025
All exercises completed, all resources documented, ready for production use.

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