It's time to write real Terraform. The language is called HCL (HashiCorp Configuration Language) and is organized into blocks. In this subsection, you'll learn about the four most important blocks you'll use constantly: resource, variable, output, and locals. With these, you can already describe real infrastructure.
What HCL looks like
Terraform code is written in files with the .tf extension. Everything in HCL is structured in blocks, which have this general form:
Don't worry about the exact syntax now; you'll see it with examples. The important thing: HCL is readable, similar to filling out a form with data. Let's go over the four key blocks.
- resource: what you want to create
The resource block is the most important of all. It describes an infrastructure resource you want to exist: a server, a network, a bucket...
Let's break it down:
resource→ the block type."aws_instance"→ the resource type (an AWS EC2 instance). Theaws_prefix indicates it's from AWS."my_server"→ the name YOU give it to refer to it within your code (it's not the name in AWS, it's an internal label).- Inside
{ }→ the arguments that configure the resource (which AMI to use, what instance type... remember Chapter 4?).
Analogy: A
resourceblock is like filling out the order form for a resource: "I want an instance, of type t3.micro, with this image." Terraform reads the form and creates it for you.
Every AWS resource you saw in Part II (EC2, S3, VPC...) has its own resource type in Terraform: aws_instance, aws_s3_bucket, aws_vpc, etc.
- variable: values that come from outside
The variable block defines configurable values that you can change without touching the rest of the code. They're like the "parameters" of your configuration.
variable "instance_type" {
description = "The EC2 instance type to use"
type = string
default = "t3.micro"
}Then you use that variable in your resources with the var.name syntax:
resource "aws_instance" "my_server" {
ami = "ami-0c1234567890abcde"
instance_type = var.instance_type # ← uses the variable
}Why they're useful: Variables make your code reusable and flexible. For example, you can use
t3.microin testing andm5.largein production by changing only the variable's value, without touching the server definition. We'll see more in Chapter 19 (environments).
Analogy: Variables are like the configurable settings of an appliance. The washing machine is the same, but you adjust the temperature and program according to the laundry. Your code is the same, but you adjust the variables according to the environment.
- output: information you want to see
The output block displays useful information after applying your configuration. It's used to "output" data you need to know, like a server's IP or a website's URL.
output "public_ip" {
description = "The server's public IP"
value = aws_instance.my_server.public_ip
}When you run terraform apply, at the end you'll see:
Why they're useful: After creating infrastructure, you need to know things like "what's my server's IP?" or "what's my database address?" Outputs show them to you automatically, without having to look them up in the AWS console. They're also used to pass information between modules (Chapter 18).
Analogy: Outputs are like the receipt or summary you get at the end: "here are the important details of what you just created."
- locals: calculated and reusable values
The locals block defines internal reusable values within your code. Unlike variables (which come from outside), locals are values you define and calculate inside your configuration.
locals {
project_name = "online-store"
environment = "production"
full_name = "${local.project_name}-${local.environment}"
}And you use them with local.name:
resource "aws_instance" "my_server" {
ami = "ami-0c1234567890abcde"
instance_type = "t3.micro"
tags = {
Name = local.full_name # → "online-store-production"
}
}When to use them: to avoid repeating values or calculations. If you use the same name or combination of values in many places, define it once in
localsand reuse it. If it changes, you change it in just one place.
Variable vs Local — the key difference:
variable: comes from outside (set by whoever uses the code). "Input parameter."local: is defined and calculated inside the code. "Reusable internal value."
Summary of the four blocks
| Block | What it's for | How to reference it |
|---|---|---|
resource |
Create infrastructure (the most important) | aws_instance.my_server |
variable |
Configurable values from outside | var.name |
output |
Show information after applying | (seen at the end of apply) |
locals |
Reusable internal values | local.name |
An example that puts it all together
variable "environment" {
type = string
default = "dev"
}
locals {
name = "myapp-${var.environment}"
}
resource "aws_instance" "web" {
ami = "ami-0c1234567890abcde"
instance_type = "t3.micro"
tags = {
Name = local.name
}
}
output "public_ip" {
value = aws_instance.web.public_ip
}This code: takes an environment as a variable, calculates a name with a local, creates a server tagged with that name, and shows its IP as an output. You're already reading Terraform!
What you should remember
- HCL is organized in blocks, written in
.tffiles. resource: the most important block; describes the infrastructure you want to create (servers, networks, buckets...).variable: configurable values that come from outside; used withvar.name. Make the code reusable.output: shows useful information afterapply(IPs, URLs...); used with the resource reference.locals: reusable internal values you define inside the code; used withlocal.name.- Key difference: variable = comes from outside; local = calculated inside.
In the next subsection, we'll look at HCL's data types (string, number, list, map, object...) to shape your variables and values.
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
