We close the chapter with a practical review of the commands you’ll use daily with Terraform. We’ve already seen some separately; here we bring them together in order, add two new ones (fmt and validate), and arrange them as a real workflow. Consider this your command cheat sheet.
The complete workflow
This is the typical order of commands in a Terraform project:
fmt → validate → init → plan → apply → ... → destroy (format) (check) (prepare) (preview) (apply) (destroy)
Let’s go through them one by one.
terraform init: prepare the project
What it does: initializes the working directory. Downloads the providers (subchapter 11.1) and configures the state backend (subchapter 11.3).
When to run it:
- The first time you work on a project.
- When you add or change a provider.
- When you change the backend configuration.
It’s the first command you run in any new project. Without it, the other commands won’t work because the providers haven’t been downloaded.
terraform fmt: format the code
What it does: automatically formats your .tf files to have a consistent style (indentation, alignment, spaces). It doesn’t change the logic, just the appearance.
Why use it: well-formatted code is easier to read and avoids style debates within the team. It’s like an automatic style checker. It’s usually run before saving or pushing changes. In teams, it’s checked automatically in CI (Chapter 22).
Analogy:
fmtis like the “tidy/align” button in a document: it makes everything neat and uniform without changing the content.
terraform validate: check that the code is correct
What it does: checks that your code is valid syntactically and logically, without connecting to AWS or creating anything. It detects errors like misspelled arguments or non-existent references.
Why use it: it warns you of errors before you try to apply anything. It’s a quick and cheap check. If
validatefails, there’s no point in continuing.
Difference with
plan:validateonly checks that the code is well written (doesn’t need credentials).plangoes further: it queries AWS and tells you what changes it would make. Firstvalidate(is it well written?), thenplan(what will it do?).
terraform plan: preview the changes
What it does: shows what changes it would make without applying them. It compares your code, the state, and reality (subchapters 9.4 and 11.2).
Remember the symbols: + create, ~ modify, - destroy. It’s your safety net: you review before touching anything.
terraform apply: apply the changes
What it does: executes the changes for real, after showing you the plan and asking for confirmation (yes).
After applying, your real infrastructure matches your code, and the state is updated.
Tip:
terraform applyalready does aplaninternally and shows it to you before asking for confirmation, so you can review one last time before typingyes.
terraform destroy: delete the infrastructure
What it does: destroys all managed resources, after confirmation. Ideal for cleaning up tests and stopping payments (subchapter 9.4).
⚠️ Irreversible. Great for testing, dangerous in production. Use with care.
Command summary table
| Command | What it does | Touches AWS? | Asks for confirmation? |
|---|---|---|---|
init |
Downloads providers and prepares backend | No (just downloads) | No |
fmt |
Formats the code | No | No |
validate |
Checks that the code is valid | No | No |
plan |
Previews changes | Reads | No |
apply |
Applies the changes | Yes (writes) | Yes (yes) |
destroy |
Deletes everything | Yes (deletes) | Yes (yes) |
Other useful commands (to know)
Besides the essentials, there are others you’ll use:
| Command | What for |
|---|---|
terraform show |
View the current state or a saved plan |
terraform state list |
List managed resources (subchapter 11.2) |
terraform output |
View defined outputs (subchapter 10.1) |
terraform refresh |
Sync state with reality |
terraform import |
Bring existing resources into state (Chapter 20) |
terraform graph |
View the dependency graph |
A real workflow, step by step
This is what your day-to-day would look like creating new infrastructure:
1. terraform init # only the first time (or when changing providers/backend) 2. (write your .tf code) 3. terraform fmt # tidy up the code 4. terraform validate # is it well written? 5. terraform plan # what will change? -> review 6. terraform apply # apply -> type "yes" 7. (use your infrastructure) 8. terraform destroy # when you no longer need it (in tests)
Tip: Get used to always running
planbeforeapplyand reading the plan carefully. It’s the habit that will save you from costly mistakes. Many cloud scares are avoided simply by carefully reading what Terraform was about to do.
What you should remember
- The typical flow is
init→fmt→validate→plan→apply→destroy. init: prepares the project (downloads providers, configures backend). First command, essential.fmt: formats the code (clean and uniform style).validate: checks that the code is valid, without touching AWS.plan: previews the changes (doesn’t apply anything). Your safety net.apply: applies the changes (asks foryes).destroy: deletes everything (asks foryes, irreversible).- Golden habit: always
planbeforeapply, and read the plan carefully.
With this, you finish Chapter 11. You now have all the theoretical pieces of Terraform: language, providers, state, and commands. In Chapter 12 we’ll put it all together by building your first real infrastructure: a VPC with an EC2 server, step by step.
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
