You already have the network ready (VPC, public subnet, Internet Gateway, and routes). Now let's add the main character: an EC2 server inside that subnet, accessible from the internet. Remember everything from Chapter 4 about instances; now we'll write it in Terraform.
What We Will Add
On top of the network from the previous subchapter, we add an EC2 instance:
┌──────────── VPC (10.0.0.0/16) ────────────┐ │ ┌─ Public Subnet (10.0.1.0/24) ─┐ │ │ │ ┌──────────────────┐ │ │ │ │ │ EC2 Instance │ ◄── new │ │ │ │ │ (web server) │ │ │ │ │ └──────────────────┘ │ │ │ └─────────────────────────────────┘ │ └────────────────────────────────────────────┘
Step 1: Find the AMI (the base image)
Remember that every instance starts from an AMI (subchapter 4.2). Instead of manually writing an AMI ID (which changes over time and by region), it's best to automatically find the most recent one with a data block:
data "aws_ami" "amazon_linux" {
most_recent = true
owners = ["amazon"]
filter {
name = "name"
values = ["al2023-ami-*-x86_64"]
}
}What is a
datablock? It's an important new concept: whileresourcecreates something, adatablock (data source) queries existing information in AWS without creating anything. Here we ask AWS "what is the most recent Amazon Linux 2023 AMI?" and save the answer to use it. It's very useful to avoid writing hardcoded values that expire.
Step 2: Define variables (best practice)
To make the code flexible (subchapter 10.1), we define some variables:
variable "tipo_instancia" {
description = "EC2 instance type"
type = string
default = "t3.micro" # eligible for free tier (Chapter 4)
}
variable "nombre_proyecto" {
type = string
default = "mi-primer-servidor"
}Using t3.micro (subchapter 4.1) is ideal for learning, because it's included in the AWS free tier.
Step 3: Create the EC2 instance
Now the main resource. We place it in our public subnet:
resource "aws_instance" "web" {
ami = data.aws_ami.amazon_linux.id # the AMI we found
instance_type = var.tipo_instancia # the variable
subnet_id = aws_subnet.publica.id # ← in the public subnet
vpc_security_group_ids = [aws_security_group.web.id] # firewall (subchap. 12.3)
tags = {
Name = var.nombre_proyecto
}
}Let's analyze the connections:
ami→ reference to the data source from step 1 (the AMI found).instance_type→ the variable from step 2.subnet_id→ reference to the public subnet from subchapter 12.1. This places the server in our network, in the public zone.vpc_security_group_ids→ reference to the Security Group (the firewall), which we will create in the next subchapter.
Each reference creates a dependency: Terraform will create the subnet and Security Group first, and then the instance.
Step 4: Install software at startup (user_data)
Often you want the server to do something as soon as it starts, like installing a web server. For that, there is user_data: a script that runs automatically the first time the instance starts.
resource "aws_instance" "web" {
ami = data.aws_ami.amazon_linux.id
instance_type = var.tipo_instancia
subnet_id = aws_subnet.publica.id
vpc_security_group_ids = [aws_security_group.web.id]
user_data = <<-EOF
#!/bin/bash
yum update -y
yum install -y httpd
systemctl start httpd
systemctl enable httpd
echo "<h1>Hello from my first server on AWS with Terraform!</h1>" > /var/www/html/index.html
EOF
tags = {
Name = var.nombre_proyecto
}
}This script: updates the system, installs the Apache web server (httpd), starts it, and creates a welcome page. When the instance is ready, it will serve a website with that message.
What we just achieved: with
user_data, the server self-configures at birth. You don't need to connect to install anything manually. This is key for automation and for autoscaling (Chapter 13): each new server prepares itself. (For complex configurations, it's better to create a ready-made AMI, remember subchapter 4.2, butuser_datais perfect to start.)
The current state
Right now we have:
✓ Complete network (subchapter 12.1) ✓ An AMI found automatically (data source) ✓ An EC2 instance in the public subnet ✓ A script that installs a web server at startup ✗ Missing: the Security Group (firewall) — subchapter 12.3 ✗ Missing: a fixed IP and the outputs — subchapters 12.3 and 12.4
If you ran plan now, Terraform would complain that the Security Group (aws_security_group.web) we referenced is missing and hasn't been created yet. We'll solve that in the next subchapter.
What you should remember
- A
datablock (data source) queries existing information in AWS without creating anything (we use it to find the most recent AMI automatically, avoiding hardcoded IDs that expire). - The instance is placed in the subnet with
subnet_id, referencing the public subnet from the previous chapter. - Using variables (like
tipo_instancia) makes the code flexible;t3.microis ideal for the free tier. user_datais a script that the server runs at startup, perfect for self-configuration (installing software, etc.) without manual intervention.- References between resources create dependencies and the automatic creation order.
In the next subchapter, we will create the missing Security Group (firewall) and assign the server a fixed Elastic IP.
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
