In the previous subchapter, we insisted on "always pinning the version" of a module. Now we'll see why this is so important and how to do it when the module is yours and lives in a Git repository. Module versioning is what allows you to use them safely and predictably as a team, without an unexpected change breaking your infrastructure.
The Problem: A Module That Changes Under Your Feet
Imagine your company has a corporate-network module in a Git repository, used by ten projects. One day, someone improves the module and changes how it creates subnets. If all ten projects automatically used "the latest version" of the module, all would be affected by that change at once, without warning. The next terraform plan for each project would show unexpected—and possibly dangerous—changes.
Without versioning:
Module changes ──► all 10 projects inherit the change INSTANTLY
(no control, no warning) ⚠️ dangerousYou need a way to say "I use this specific version of the module, and it won't change until I decide to update." That's what versions are for.
The Solution: Versioning with Git Tags
A Git tag is a named marker you put on a specific point in a repository's history. It's used to mark versions. The most widespread convention is semantic versioning: numbers like v1.4.2.
Module repository history: ... commits ... ─► [tag v1.0.0] ... more commits ─► [tag v1.1.0] ... more commits ─► [tag v2.0.0]
Each tag is a stable "snapshot" of the module at a given moment. Once created, it does not change: v1.0.0 will always be exactly that code.
Semantic Versioning: What the Numbers Mean
The format vMAJOR.MINOR.PATCH (e.g., v2.3.1) communicates the type of change:
| Part | When it increases | Means |
|---|---|---|
| MAJOR (2.x.x) | Incompatible changes | May break users; be careful when updating |
| MINOR (x.3.x) | New compatible functionality | Adds features without breaking existing ones |
| PATCH (x.x.1) | Bug fixes | Fixes issues, safe to update |
So, at a glance, you know the risk of updating: going from
v1.2.0tov1.3.0(minor change) is safe; moving tov2.0.0(major change) may require adjustments.
How to Use a Specific Version
When referencing a module from a Git repository, you specify which version (tag) you want to use with ref:
module "network" {
source = "git::https://github.com/my-company/modules.git//network?ref=v1.2.0"
# ... ▲
# the version (tag) I use
}And for Registry modules (subchapter 18.3), with the version argument:
In both cases, you tell Terraform: "use exactly this version." Even if the module evolves, your project will keep using the version you pinned, stable and predictable, until you decide to change the number.
Why This Changes Everything
With versioning, each project controls when to update:
With versioning: Module releases v2.0.0 ──► projects stay on v1.x (no changes) Project A decides to update to v2.0.0 when ready, tests it, and upgrades. Project B stays safely on v1.x until it suits them.
Advantages:
- Stability: your infrastructure doesn't change unless you decide.
- Controlled updates: you upgrade when you're ready, reviewing the plan (subchapter 12.5) and testing first.
- Reproducibility: anyone running your code gets the same result, because the module version is pinned.
- Safe teamwork: different projects use different versions without interfering with each other.
Real-world example: the platform team publishes
corporate-network v2.0.0with an important improvement that requires adjustments (major change). Instead of imposing it on everyone at once, each project team migrates to v2.0.0 when they can: they read the release notes, test in their development environment, review the plan, and then update production. No one is caught off guard. Versioning turns a potentially chaotic update into an orderly process.
The Connection with Team Workflow
This fits perfectly with what we saw in subchapter 12.5 (PR review of plans). Updating a module version is a code change that goes through a Pull Request: you change ref=v1.2.0 to ref=v2.0.0, CI shows the plan with what that implies, a teammate reviews it, and only then is it applied. Versioning + plan review = safe and traceable infrastructure changes.
What You Should Remember
- Without versioning, a change in a shared module would instantly affect all projects using it, without control: dangerous.
- A Git tag marks a stable and immutable version of the module. The convention is semantic versioning:
vMAJOR.MINOR.PATCH. - The numbers communicate the risk: MAJOR = incompatible changes (be careful), MINOR = new compatible functionality, PATCH = safe fixes.
- Pin the version with
ref=vX.Y.Z(Git modules) orversion = "X.Y.Z"(Registry): your project uses exactly that version until you decide to change it. - Benefits: stability, controlled updates, reproducibility, and safe teamwork. Fits with the PR review of plans workflow (subchapter 12.5).
In the last subchapter of the chapter, we'll look at an important design dilemma: when to create generic modules (highly reusable) versus domain-specific modules.
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
