We close the chapter on modules with a design question that distinguishes a good infrastructure engineer: should I make my modules very generic (usable for everything) or domain-specific (that solve exactly my case)? There is no single answer, but understanding the balance will help you design modules that truly make work easier, instead of complicating it.
Two Design Philosophies
When you create a module, you face a decision about how much flexibility to offer:
Generic Module
A generic module tries to serve many different cases. It exposes many input variables so that whoever uses it can configure everything. It is flexible and highly reusable.
"generic-server" module with 40 variables: type, operating system, disk, network, security, monitoring, backups, scaling, tags... etc.
Advantage: it works for almost any situation. Drawback: it is complex to use, because the caller has to understand and fill in many options, and can make mistakes.
Domain-Specific Module
A domain-specific module solves a specific case for your organization, with decisions already made. It exposes few variables because most things are set according to your company's conventions.
"company-web-server" module with 3 variables: name, environment, size (everything else is already decided according to company standards)
Advantage: very easy to use, and automatically applies your company's best practices. Drawback: it only works for that specific case; it is not reusable outside your context.
The Analogy: Watchmaker's Tool vs Swiss Army Knife
A generic module is like a complete professional toolbox: it has everything and works for any job, but you need to know which tool to pick and how to use it. A domain-specific module is like a kit ready for a specific task ("picture hanging kit"): it only brings what you need, with simple instructions, and solves that problem without you having to think. To hang a picture, the kit is more convenient; for varied jobs, you need the full toolbox.
The Problem with Being Too Generic
A very common beginner's mistake is trying to make everything super generic "just in case." The result is a module with dozens of variables that is as complicated to use as writing the resources by hand. You've added complexity without adding value.
Module with 50 variables "just in case": → so hard to configure that nobody wants to use it → the goal of modules (simplifying) is lost ⚠️
Golden rule: a module should hide complexity, not add it. If using your module is as hard as not using it, something is wrong with the design.
The Problem with Being Too Specific
The opposite extreme also has its risk: if you make modules so specific that each one serves only a single project, you end up with many modules that are almost identical and little real reuse. You lose part of the benefit of modules.
The Balance: The Practical Rule
The key is in the balance, and a good strategy is the two-layer approach:
Layer 1 — GENERIC Modules (base, reusable)
"vpc-module", "server-module" (flexible, few decisions made)
│ used by...
▼
Layer 2 — Domain-SPECIFIC Modules (wrap the generic ones)
"company-network", "company-web-server"
(apply company conventions, easy to use)
│ used by...
▼
The teams of each project (who only pass 2-3 values)- Generic modules (often from the Registry, subchapter 18.3, or your own base modules) provide flexibility and reusable logic.
- Domain-specific modules wrap them, setting your company's decisions (security, names, regions...) and exposing only what's necessary. These are what the teams use.
Real world example: a company's platform team uses the generic VPC module from the Registry (which has 40 options). But they don't force each team to deal with those 40 options. Instead, they create a specific
company-networkmodule that internally calls the generic one with all corporate decisions already made (ranges, tags, security), and only asks the user for anameand anenvironment. Teams get a perfect network by passing two values, and the flexibility of the generic module is still there underneath when needed.
Questions to Decide How to Design Your Module
When you create a module, ask yourself:
- Who is going to use it? If it's many non-expert teams → go for specific and easy. If it's for the platform team → it can be more generic.
- How many times and in how many contexts will it be used? Highly reused in varied contexts → more generic. For a specific case in your company → specific.
- Am I hiding complexity or adding it? If you're adding complexity, rethink it.
- Is every variable I expose really necessary? When in doubt, fewer variables: start simple and add flexibility only when it proves necessary.
What You Should Remember
- When designing a module, you choose how much flexibility to offer: generic (many variables, works for a lot, but complex to use) or domain-specific (few variables, easy, but only for your case).
- Like tools: the generic is the full toolbox (versatile but requires know-how); the specific is the kit for a task (convenient and straightforward).
- Common mistake: being too generic "just in case" and creating a module so complicated that it doesn't simplify anything. A module should hide complexity, not add it.
- Best strategy: two layers. Generic modules (base/Registry) that provide flexibility, wrapped by domain-specific modules that set company conventions and are easy to use.
- When in doubt, start simple (few variables) and add flexibility only when it proves necessary.
You've finished Chapter 18! You now know how to design, version, and compose modules like a professional. In Chapter 19 we'll see how to manage multiple environments (development, staging, production) with Terraform: workspaces, directory strategies, and Terragrunt.
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
