We close the introductory chapter of Terraform with its fundamental workflow: the plan → apply → destroy cycle. These three commands are the heart of day-to-day work with Terraform. Understanding what each one does will give you the confidence to work safely, without fear of accidentally breaking anything.
The idea: see before you do
Terraform's philosophy is "look before you leap". Before touching anything in your real cloud, Terraform shows you exactly what it will do and waits for your approval. This avoids surprises and disasters.
The cycle has three main commands (plus a preparation one that we’ll see in Chapter 11):
Let’s look at the three main players.
terraform plan: the preview
The terraform plan command compares what you’ve written in your code with what currently exists in the cloud, and shows you a summary of the changes it would make, without applying anything yet.
Analogy:
planis like the print preview of a document, or the order summary before paying in an online store. You see exactly what will happen before confirming. If something doesn’t add up, you cancel with no consequences.
Terraform classifies changes with very clear symbols:
| Symbol | Meaning |
|---|---|
+ |
A new resource will be created |
~ |
An existing resource will be modified |
- |
A resource will be destroyed |
-/+ |
Will be replaced (destroyed and recreated) |
Example of
planoutput:Plan: 3 to add, 1 to change, 0 to destroy. + aws_instance.web (create a server) + aws_security_group.web (create a firewall) + aws_eip.web (create a static IP) ~ aws_s3_bucket.datos (modify a bucket)This tells you very clearly: I’m going to create 3 things and modify 1. Nothing has been touched yet. You decide whether to proceed.
Why plan is so valuable:
- Safety: you see the changes before applying them. If you see a
- aws_db_instance(destroy your database) you weren’t expecting, you can stop in time! - Team review: the result of
plancan be reviewed with teammates before applying (we’ll see this in Chapter 12 and in CI/CD in Chapter 22). - Drift detection:
planalso warns you if something was changed manually in the cloud and no longer matches your code.
terraform apply: execute the changes
The terraform apply command actually makes the changes. First, it shows you the plan again and asks for confirmation (you have to type yes). Only then does it create, modify, or destroy resources in your real cloud.
Analogy:
applyis pressing the "Confirm order" button. Until you confirm, nothing is charged or shipped.
terraform apply → shows the plan again → "Do you want to perform these actions? Type 'yes': " → you type "yes" → Terraform creates/modifies/destroys the resources → "Apply complete! Resources: 3 added, 1 changed, 0 destroyed."
After apply, your real infrastructure matches what you declared in the code. Terraform also records what it did in a state file (the tfstate, which we’ll see in Chapter 11), so it knows what it manages.
Remember idempotence (subchapter 9.2): if you run
applyagain without changing the code, Terraform will say "no changes" and do nothing. It only acts when there are differences between your code and reality.
terraform destroy: delete everything
The terraform destroy command removes all the infrastructure that Terraform manages. Like apply, it shows you what it’s going to destroy and asks for confirmation.
Analogy:
destroyis deleting the entire project and leaving the table clean. Everything Terraform created, it deletes.
What is destroy for?
- Clean up test environments: you set up an environment to experiment, test, and when you’re done,
destroydeletes everything. You stop paying instantly (remember Chapter 1: pay as you go). - Temporary environments: infrastructure you only need for a while.
Huge advantage for learning: While following this book and practicing, you can create infrastructure with
apply, experiment, and then cleanly delete it withdestroyso you don’t rack up costs. It’s one of the great advantages of IaC: what you create, you destroy without leaving a trace or forgotten resources costing you money.
⚠️ Be careful in production:
destroyis irreversible and destroys everything managed by that configuration. Never run it lightly in a real environment. In production, critical resources are protected and restrictions are set on who can destroy. It’s a great tool for testing, dangerous in production.
The complete cycle in practice
This is what a typical day working with Terraform looks like:
1. You write or modify your code (.tf files).
2. terraform plan → you review what will change.
3. Does the plan look good?
Yes → terraform apply → confirm → changes applied.
No → fix the code and go back to step 2.
4. (When you no longer need it, in tests)
terraform destroy → delete everything and stop paying.You’ll repeat this plan → apply cycle constantly: every time you want to change your infrastructure, you modify the code, review with plan, and apply with apply. It’s safe, predictable, and keeps a record of everything.
What you should remember
- The fundamental Terraform workflow is plan → apply → destroy (preceded by
init, which we’ll see in Chapter 11). plan: previews the changes without applying them (like the order summary before paying). Uses symbols:+create,~modify,-destroy.apply: executes the changes after asking for confirmation (yes). Leaves your cloud as you declared.destroy: removes all managed infrastructure. Great for cleaning up tests and stopping costs; dangerous in production.- The philosophy is "see before you do": you review with
planbefore confirming withapply. This makes the work safe and predictable.
With this, you finish Chapter 9. You now understand why IaC exists and how Terraform works at a high level. In Chapter 10 we’ll roll up our sleeves and learn HCL, the language you’ll use to write your infrastructure.
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
