We close the chapter on environment management by looking in detail at how values are passed to Terraform: .tfvars files and environment variables. We’ve already mentioned them in passing; now you’ll fully understand how to separate configuration (the values that change) from code (the logic that stays the same). This separation is key to managing environments cleanly.
Reminder: Variables
Remember the variables from Chapter 10. A variable is a “placeholder” in your code that gets filled with a value from outside:
variable "tipo_instancia" {
description = "EC2 instance type"
type = string
}
variable "cantidad_servidores" {
type = number
default = 1
}The code uses var.tipo_instancia, but does not set the value. Where does that value come from? There are several ways to provide it, and we’ll see them here.
.tfvars Files: The Main Method
A .tfvars file is a file where you assign values to variables. It’s the most common and organized way to configure an environment. For example:
# produccion.tfvars tipo_instancia = "t3.large" cantidad_servidores = 5 nombre_proyecto = "tienda-prod"
# desarrollo.tfvars tipo_instancia = "t3.micro" cantidad_servidores = 1 nombre_proyecto = "tienda-dev"
Notice the key idea: the code is the same, but each .tfvars file gives it different values. This is exactly what allows you to use the same logic for multiple environments (subchapter 19.2).
How to Use a .tfvars
You tell Terraform which values file to use with the -var-file option:
terraform apply -var-file="produccion.tfvars" # applies with production values terraform apply -var-file="desarrollo.tfvars" # applies with development values
Special case —
terraform.tfvars: if you name a file exactlyterraform.tfvars, Terraform loads it automatically without you having to specify it. That’s why, in the directory strategy (subchapter 19.2), each environment folder usually has its ownterraform.tfvarsthat is applied automatically.
Analogy: Terraform code is like a letter template with blanks (“Dear ___, your order ___ is ready”). The
.tfvarsfiles are the data that fill in the blanks for each recipient. One template, many personalized letters. You change the data, not the template.
Environment Variables: Another Way to Pass Values
Another way to provide values to variables is through operating system environment variables, using the TF_VAR_ prefix:
Terraform automatically reads any environment variable that starts with TF_VAR_ and assigns it to the corresponding variable (here, tipo_instancia).
When is this used? Mainly in two cases:
- In automation (CI/CD, Chapter 22): automated systems often pass values as environment variables, without files.
- For sensitive data (secrets): as we’ll see shortly, passwords and keys should not go in
.tfvarsfiles that are pushed to Git; environment variables are a way to pass them without writing them in any repository file.
The Order of Precedence
Since there are several ways to provide values, Terraform follows an order of precedence if the same value is defined in multiple places (from lowest to highest priority, roughly):
1. "default" value in the variable definition (lowest) 2. terraform.tfvars file (automatic) 3. -var-file files you specify 4. TF_VAR_ environment variables 5. -var option on the command line (highest)
You don’t need to memorize it, but remember: if you define a value in several places, the most specific/direct one wins. If in doubt, what you put on the command line overrides everything else.
⚠️ Security: Never Put Secrets in Versioned .tfvars
This is critical. Your .tfvars files with normal configuration (sizes, names) can be safely stored in Git. But you should NEVER put sensitive data —database passwords, API keys, tokens— in .tfvars files that you push to the repository. If you do, those secrets are exposed in the Git history to anyone who accesses it.
❌ BAD: password = "MyPassword123" in a .tfvars file pushed to Git
✅ GOOD: the secret is passed via environment variable, or better yet,
read from a secrets manager (Secrets Manager, Ch. 23)The correct ways to handle secrets:
- Environment variables (
TF_VAR_password), which are not stored in any repo file. - Secrets managers like AWS Secrets Manager or Parameter Store (which we’ll see in Chapter 23): Terraform reads them at runtime, so the secret is never written in the code.
- Add
.tfvarsfiles with secrets to.gitignoreso they are never pushed.
Also remember (from Chapter 11) that the state itself can contain sensitive data, so the remote backend must be encrypted and access-restricted. Secret management is a serious topic that we’ll revisit in depth in Chapter 23.
What You Should Remember
- Variables leave “blanks” in the code; values are provided from outside, separating configuration from code (the same logic works for multiple environments).
.tfvarsfiles are the main way to assign values; you use-var-file="environment.tfvars", and a file namedterraform.tfvarsis loaded automatically.- Environment variables with the
TF_VAR_prefix are another way to pass values, useful in CI/CD and for secrets. - There is an order of precedence when a value is defined in multiple places: the most direct wins (command line > environment > files > default).
- ⚠️ Critical security: never put secrets (passwords, keys) in
.tfvarsfiles versioned in Git. Use environment variables, secrets managers (Secrets Manager, Chapter 23), and.gitignore.
You’ve finished Chapter 19! You now know how to manage multiple environments cleanly and securely. In Chapter 20 we’ll dive deeper into one of the most important topics for teamwork: remote backends and state locking.
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
