In the previous subchapter, we saw that the directory-per-environment strategy is excellent, but it has a small drawback: some repetition between environments. When you have many environments or many components, that repetition grows. Terragrunt is a community tool that solves exactly that, helping you keep your code DRY. Let’s see what it is and when it’s worth it.
The DRY Principle
DRY stands for "Don't Repeat Yourself". It’s a fundamental programming principle: each piece of information or logic should exist in one single place. Repeating code is bad because, if you need to change something, you have to change it in many places (and it’s easy to forget one).
Repeated (bad): the same configuration code in dev, stg, prod DRY (good): the common configuration in ONE place, each environment only its differences
In subchapter 19.2, we saw that the directory strategy leaves some repetition in the main.tf of each environment (backend configuration, calls to similar modules...). Terragrunt tackles that repetition.
What is Terragrunt
Terragrunt is a tool that wraps Terraform (a "wrapper"), created by the company Gruntwork. It does not replace Terraform: it complements it, adding features to better manage multiple environments and reduce repetition.
Terragrunt ──(uses underneath)──► Terraform
(reduces repetition, (does the real work)
manages environments)Analogy: if Terraform is the engine, Terragrunt is a driver assistance system that sits on top: the engine still does the work, but driving becomes more comfortable and you make fewer mistakes, especially on long and repetitive journeys (many environments).
What Problems Does Terragrunt Solve
- Backend Configuration Without Repetition
Remember that each environment needs to configure its state backend (Chapter 11, subchapter 20.1), and that is repeated in each folder. With Terragrunt, you define the backend configuration only once and each environment inherits it, automatically generating the part that changes (for example, the state path for each environment).
- Common Values in One Place
Values shared by all environments (region, common tags, accounts...) are defined once and inherited, instead of being copied into each environment.
- Less Repeated Code Per Environment
Each environment is reduced to a small file that says "use this module with these specific values," while all the common machinery (backend, providers, configuration) is centralized. The files for each environment are minimal: only their differences.
Typical structure with Terragrunt: terragrunt.hcl ← common configuration (backend, region...) ONCE environments/ ├── dev/terragrunt.hcl ← only: "use module X with these dev values" ├── stg/terragrunt.hcl ← only the stg differences └── prod/terragrunt.hcl ← only the prod differences
- Managing Dependencies Between Components
Terragrunt also helps orchestrate multiple infrastructure components (network, database, application) and their dependencies, applying them in order, which is very useful in large projects.
When to Use Terragrunt (and When Not To)
Like any tool, Terragrunt adds its own complexity (another tool to learn and maintain). The decision to adopt it depends on the size of your project:
| Situation | Terragrunt? |
|---|---|
| Small project, 1-2 environments | ❌ Not needed; directory strategy is enough |
| You’re starting with Terraform | ❌ Learn Terraform first |
| Many environments and/or many components | ✅ Reduces a lot of repetition |
| Repeated configuration that hurts | ✅ Its strong point |
| Large team with complex infrastructure | ✅ Brings order and consistency |
Important tip: don’t adopt Terragrunt from day one. Start with Terraform and the directory strategy from subchapter 19.2. Only when you feel that repetition between environments becomes a real problem, consider Terragrunt. Adopting it too soon adds unnecessary complexity (remember the lesson from Chapter 17 about not choosing complex tools just because they’re trendy).
A Note on Alternatives
It’s worth knowing that Terragrunt is not the only option. Terraform itself has been adding improvements, and platforms like Terraform Cloud / HCP Terraform (which we’ll see in Chapter 22) also help manage environments. Terragrunt is still very popular, but the ecosystem evolves; choose according to what your team needs and knows.
What You Should Remember
- DRY ("Don't Repeat Yourself") is the principle of not repeating code: each thing should be in one single place, so you don’t have to change it in many places.
- Terragrunt is a tool that wraps Terraform (does not replace it) to reduce repetition when managing multiple environments. Like a "driver assistant" on top of the Terraform engine.
- It solves: backend configuration without repetition, common values in one place, less code per environment (only the differences), and orchestration of dependencies between components.
- Don’t adopt it from the start: begin with Terraform + directories per environment (subchap. 19.2), and switch to Terragrunt only if repetition becomes a real problem. Don’t add complexity just because it’s trendy.
In the last subchapter of the chapter, we’ll see how to manage values for each environment in detail: environment variables and .tfvars files.
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
