We close the chapter on backends and state with a very useful command in the real world: terraform import. It solves a very common situation: you have resources in AWS that were created manually (or with another tool) and you want Terraform to start managing them. Instead of deleting and recreating them, you "adopt" them into your state. Let's see how.
The problem: infrastructure that already exists
Few companies start from scratch with Terraform. Usually, they already have infrastructure created before adopting Infrastructure as Code:
- Resources created manually from the AWS console (by clicking).
- Resources created with old scripts or other tools.
- "Inherited" infrastructure that nobody really remembers how it was set up.
Now you want to manage all that with Terraform (remember the advantages from Chapter 9). But there's a problem: Terraform doesn't know about those resources, because they're not in its state. If you simply write the code and run apply, Terraform would try to create new ones, duplicating what already exists! And you can't just delete the originals, because they're in production.
Real resource in AWS (created manually) ✓ exists Terraform state ✗ doesn't know it → if you apply, Terraform creates a DUPLICATE ⚠️
The solution: terraform import
terraform import solves this: it tells Terraform "this resource that already exists in AWS, register it in your state and associate it with this block in my code." That way, Terraform starts to manage the existing resource, without creating it again.
Analogy:
importis like adopting something that already exists. Imagine you move into a house that already has furniture. You don't throw it away to buy identical pieces: you register them as yours and start managing them.terraform importdoes that with infrastructure: it adopts what's already there instead of recreating it.
How it works, step by step
Importing a resource has two parts that you must do together:
Step 1: Write the code block
First, you write in your Terraform code the resource block that will represent that existing resource. For example, if you have an EC2 instance created manually:
resource "aws_instance" "servidor_heredado" {
# for now, it can be pretty empty;
# you'll complete it to match the real resource
}Step 2: Run the import
Then, you run the import command indicating two things: which block in your code it corresponds to, and the real ID of the resource in AWS:
terraform import aws_instance.servidor_heredado i-0a1b2c3d4e5f67890
│ │
│ └── Real ID in AWS
└── your code blockTerraform looks for that instance (i-0a1b2c3d4e5f...) in AWS and registers it in the state, associated with your servidor_heredado block. From now on, Terraform knows that resource belongs to it.
Step 3: Complete the code to match
Here's the part that requires care. After importing, your code must describe the resource exactly as it really is. If your code doesn't match the real resource, the next plan would show changes (Terraform would want to "adjust" the resource to your incomplete code).
After importing: terraform plan → if the code matches reality → "No changes" ✓ → if it DOESN'T match → shows differences; adjust your code until it fits
The goal is to get a plan that says "no changes": that confirms your code exactly reflects the real resource, and that the adoption was clean.
The challenge: importing lots of infrastructure
Importing one resource is simple. The problem comes when you have to import hundreds of inherited resources, writing the code for each one and adjusting it by hand. It's a tedious job. That's why there are aids:
importblocks in the code (modern versions of Terraform): allow you to declare imports directly in the code in a more organized way, and even generate a draft of the resource code automatically.- Third-party tools like Terraformer, which scan your AWS account and generate the code and state for many resources at once. Useful for large migrations, though it's always best to review the result.
Real-world example: a company has been creating infrastructure manually in AWS for years and decides to "bring order" by adopting Terraform. Instead of recreating everything (impossible, it's in production), they import their key resources into Terraform state, writing the code that describes them, until
plansays "no changes." From then on, they manage that infrastructure with code, with all the advantages from Chapter 9, without having caused any interruption.
When to use import (and a warning)
- Use it when you have valuable existing resources (in production) that you want to manage with Terraform without recreating them.
- Do it carefully: importing touches the state (subchapters 20.2 and 20.3), so apply the same precautions: backup, no one else working, and verify with
planat the end. - Don't obsess over importing absolutely everything at once. It's valid to adopt resources little by little, starting with the most important ones.
What you should remember
terraform importallows Terraform to adopt resources that already exist in AWS (created manually or with another tool), registering them in its state without recreating them.- Without import, if you write the code for a resource that already exists and run
apply, Terraform would create a duplicate. Import avoids that. - Process: write the
resourceblock, runterraform import <block> <real-ID>, and complete the code untilplansays "no changes" (sign of a clean adoption). - Like adopting furniture in a new house instead of throwing it away and buying identical pieces.
- For many resources, there are aids:
importblocks (which generate code drafts) and tools like Terraformer. - Apply state management precautions (backup, verification plan) and adopt resources little by little, starting with the most important ones.
You've finished Chapter 20! You now thoroughly master Terraform state: backends, locking, migration, and import. In Chapter 21 we'll see how to ensure the quality and security of your infrastructure with testing: validation, security analysis, and integration tests.
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
