Welcome to Part III, where you will learn Terraform and Infrastructure as Code (IaC). But before touching Terraform, you need to understand what problem it solves. And for that, let's talk about "manual provisioning": the traditional way of creating infrastructure, with all its pains. When you understand these problems, you will truly appreciate why IaC has changed the rules of the game.
What is manual provisioning
Provisioning infrastructure means creating and configuring the resources your application needs: servers, networks, databases, permissions...
Manual provisioning is doing it by hand, clicking in the AWS web console. You go to the website, click "create instance," fill out forms, configure networks by hand, adjust permissions one by one... It's how you've been imagining doing things in the previous chapters.
It works for learning or for a quick test. But for real projects, it has serious problems.
Problem 1: It's slow and repetitive
Creating a complete environment by hand (network, subnets, servers, database, permissions...) can take hours of clicking through dozens of screens. And if you need another identical environment (for example, a test environment identical to production), you have to repeat the whole process from scratch.
Example: Your boss asks you to set up a test environment identical to production. By hand, that means meticulously recreating each resource, trying to remember exactly how you configured the original three months ago. Hours of tedious and error-prone work.
Problem 2: It's prone to human error
When you configure dozens of resources by hand, it's easy to make mistakes: you forget to check a box, mistype a value, configure a permission incorrectly. And errors in infrastructure can cause outages or security holes.
Real example: You configure the test environment and, by mistake, open the SSH port to the entire internet (the error from Chapter 4). In production you did it right, but in testing you forgot. Now you have a vulnerability... and you don't even know it, because you "thought" you did it the same.
Problem 3: Environments "drift" (drift)
When everything is done by hand, it's impossible to guarantee that two environments are identical. Over time, someone makes a quick change in production "to fix something" and doesn't replicate it in testing. The environments silently diverge.
This causes the classic: "it works in testing, but fails in production" (or vice versa). The reason is that, in reality, they weren't the same, even if no one knew it. This divergence is called drift (configuration drift).
Problem 4: There's no record of what exists or why
With manual provisioning, no one knows for sure what's deployed, who created it, when, or why. The knowledge lives in the head of whoever set it up (or in scattered notes).
Example: You find a running server that costs money every month. No one remembers what it's for or if it can be turned off. Do you turn it off and risk breaking something, or do you keep paying "just in case"? Without documentation, you're flying blind.
Problem 5: Hard to review, version, and revert
- No review: a change made by hand doesn't go through any control. A person can modify production without anyone reviewing it.
- No history: there's no record of "what changed and when," as there is with code.
- No easy rollback: if a change breaks something, there's no "undo" button. You have to remember and revert each step by hand.
Problem 6: Doesn't scale with the team
When there are several people, manual provisioning becomes chaos:
- Two people can change the same thing at the same time and overwrite each other.
- No one knows what each person touched.
- The "expert who set it up" becomes a bottleneck (and a risk if they leave the company).
The root of all these problems
If you notice, all these pains share a cause: the infrastructure is not written or recorded anywhere reliable. It's "done by hand" and exists only in the AWS console and in people's memory.
What if we could describe all our infrastructure in text files, as if it were code? Then we could:
- Reuse it (create identical environments instantly).
- Version it (change history, like in Git).
- Review it (have someone else validate before applying).
- Revert it (go back to a previous version).
- Automatically document it (the code IS the documentation).
That idea is exactly Infrastructure as Code, and that's what we'll see in the rest of Part III.
Summary table: manual vs what we need
| Problem with manual provisioning | What we need |
|---|---|
| Slow and repetitive | Create environments instantly, repeatable |
| Human errors | Consistent and reviewed configuration |
| Drift (diverging environments) | Guaranteed identical environments |
| No one knows what exists | Infrastructure documented in code |
| No history or rollback | Versioning and change history |
| Doesn't scale with team | Collaboration with review |
What you should remember
- Manual provisioning (creating infrastructure by clicking in the console) is good for learning, but not for serious projects.
- Its problems: it's slow and repetitive, prone to errors, causes drift (diverging environments), leaves no record of what exists, and doesn't scale with a team.
- The root cause: the infrastructure is not written anywhere reliable, only in the console and in people's memory.
- The solution is to describe the infrastructure in text files (code): reusable, versionable, reviewable, and reversible. That's Infrastructure as Code.
In the next subchapter, we'll see a key nuance of IaC: the difference between the declarative and imperative approach, and why Terraform chooses the declarative one.
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
