We already know that we want to describe infrastructure as code (IaC). But there are two ways to do it: the imperative approach and the declarative approach. Understanding this difference is key to understanding how Terraform "thinks" and why it is so convenient. It's a simple concept with an analogy that makes it crystal clear.
The Two Philosophies
- Imperative: you describe STEP BY STEP how to achieve the result. You give detailed instructions in order: "do this, then this, then that."
- Declarative: you describe WHAT result you want, and let the tool figure out how to achieve it. You only state the destination, not the path.
Taxi Analogy (the key to everything):
- Imperative: you give the taxi driver step-by-step directions: "turn right, go straight for 200 meters, at the roundabout take the second exit, turn left...". If you make a mistake in a step, you end up in the wrong place.
- Declarative: you tell the taxi driver "take me to the airport". He decides the best route. If there's a traffic jam, he finds another way. You only expressed the desired destination.
Terraform is declarative: you declare the destination (the infrastructure you want) and it figures out the steps.
Imperative: "how" to do it
An imperative approach would be, for example, a script (in Bash, Python...) that executes commands in order:
1. Create a network. 2. Create a subnet within the network. 3. Launch a server in the subnet. 4. Configure the server's firewall. 5. ...
Problems with the imperative approach:
- You manage the order and all dependencies.
- What happens if you run it twice? It might try to create things that already exist and fail, or duplicate them. You have to write logic to check "does this already exist?" at every step.
- What if something changes? You have to write more instructions to detect and modify the current state.
It's like step-by-step directions: it works, but you're responsible for every detail and every unexpected event.
Declarative: "what" I want
With the declarative approach (Terraform's), you write the desired final state:
I want: - A network with this address range. - A subnet within it. - A server of this type in that subnet. - A firewall with these rules.
You don't say in what order to create it or how. You simply declare what you want to exist. Terraform takes care of the rest:
- Calculates the correct order and dependencies automatically (it knows the subnet goes inside the network, so it creates the network first).
- Compares what you've declared with what already exists.
- Makes only the necessary changes so that reality matches your declaration.
The Big Advantage: Idempotence
Here's the key word of the declarative approach: idempotence.
Idempotence means you can apply the same configuration many times and the result is always the same. No matter how many times you run it: if it's already in the desired state, it does nothing; if something is missing, it creates it; if something differs, it adjusts it.
Practical example: You declare that you want "1 server".
- You apply it: Terraform sees there are none and creates 1.
- You apply it again (without changing anything): Terraform sees there is already 1 and does nothing. It doesn't create a second server by mistake.
- You change the declaration to "3 servers" and apply: Terraform sees there is 1 and adds 2 more to reach 3.
You always end up with exactly what you declared. This is the opposite of an imperative script, which could create duplicate servers every time you run it.
Idempotence is what makes declarative IaC safe and predictable. It directly solves the problem of "what happens if I run it again?".
Comparison
| Aspect | Imperative | Declarative (Terraform) |
|---|---|---|
| Describes | The how (steps) | The what (desired result) |
| Order management | You | The tool |
| Idempotent | Not by default (you have to program it) | Yes, naturally |
| Change detection | Manual | Automatic |
| Example | Step-by-step Bash/Python script | Terraform, CloudFormation |
| Analogy | Turn-by-turn directions | "Take me to the airport" |
Is Imperative Bad? No, but...
The imperative approach has its place (one-off scripts, specific automations). But to manage infrastructure, the declarative approach is far superior because:
- It's easier to read: the code directly describes how everything should end up.
- It's idempotent and safe to re-run.
- The tool manages the complexity (order, dependencies, changes) for you.
That's why the main modern IaC tools, including Terraform, are declarative.
Note: some modern tools (like AWS CDK or Pulumi, which we'll see in subchapter 9.3) use regular programming languages, which seem imperative, but underneath generate a declarative model. The important thing is the result: you declare the desired state.
What You Should Remember
- Imperative: you describe how (the steps, in order). You manage the order and repetitions.
- Declarative: you describe what you want (the final result). The tool figures out the how. This is the Terraform approach.
- The analogy: imperative = turn-by-turn directions; declarative = "take me to the airport".
- The big declarative advantage is idempotence: applying the same configuration many times always gives the same result, with no duplicates or surprises.
- To manage infrastructure, the declarative approach is more readable, safe, and predictable.
In the next subchapter, we'll compare the most important IaC tools —Terraform, CloudFormation, Pulumi, and CDK— and see why we choose Terraform.
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
