We have seen checks that analyze the code without executing it (fmt, validate, Checkov, tfsec). They are fast and useful, but they have a limit: they do not check that your infrastructure actually works once created. For that, there are integration tests, and the most well-known tool in the Terraform world is Terratest. In this subsection, you will understand what they provide and how they work in broad strokes.
The limit of static analysis
The tools from the previous subsections read your code, but do not create anything. They can tell you "this code looks correct and safe," but they cannot answer questions like:
- Does the EC2 instance actually start up and respond?
- Does the web server return the expected page?
- Does the load balancer distribute traffic correctly?
- Do the resources connect to each other as they should?
To answer this, it is not enough to read the code: you have to actually create the infrastructure and test it. That is an integration test.
What is an infrastructure integration test
An integration test follows this cycle: create the real infrastructure (in a test environment), verify that it works as expected, and then destroy it so nothing is left behind and you stop paying.
1. CREATE → terraform apply (sets up the real infra in a test account) 2. VERIFY → check that it works (does it respond? is it properly configured?) 3. DESTROY → terraform destroy (cleans everything up, stops paying)
Analogy: static analysis is like reviewing the blueprints of a car; integration testing is like building a prototype and driving it on a test track to see if it really starts, brakes, and turns. After the test, the prototype is dismantled. It's more expensive than looking at the blueprints, but it gives you a certainty that paper cannot provide.
What is Terratest
Terratest is a Go library (created by Gruntwork) for writing infrastructure integration tests. With it, you write a small program in the Go language that automates the entire cycle: applies your Terraform, performs checks, and destroys the infrastructure at the end.
A test with Terratest, broadly speaking, does something like this (you don't need to master Go to get the idea):
// Simplified pseudo-example of a test with Terratest
func TestWebServer(t *testing.T) {
// 1. Apply the Terraform
terraform.InitAndApply(t, options)
// 3. Ensure it is destroyed at the end (even if the test fails)
defer terraform.Destroy(t, options)
// 2. Verify: get the IP from the output and check that the web responds
ip := terraform.Output(t, options, "public_ip")
http.Get("http://" + ip) // Does it respond 200 OK with the expected content?
}Notice the pattern:
- Apply: creates the infrastructure (uses your real Terraform code).
- Verify: reads the outputs (remember subsection 12.4) and checks real things, like whether the web server responds.
- Destroy (with
defer): ensures cleanup always happens, even if the test fails. This is crucial to avoid leaving costly resources forgotten.
What you can verify with Terratest
Terratest allows you to check that the infrastructure actually works:
- That a website responds with the expected code and content.
- That a server is accessible via SSH or a port.
- That a database accepts connections.
- That the outputs of Terraform have the correct values.
- That a module (Chapter 18) creates exactly the resources it promises.
It is especially useful for testing reusable modules: before publishing a new version of your module (subsection 18.4), an integration test confirms that it still works.
The trade-off: powerful but costly
Integration tests are the most comprehensive, but also the most costly, and it is important to be aware of this:
| Static analysis (fmt, validate, Checkov) | Integration tests (Terratest) | |
|---|---|---|
| Creates real infrastructure | No | Yes |
| Speed | Seconds | Minutes (creates and destroys) |
| Cost | Free (creates nothing) | Costs money (real resources) |
| What it tests | That the code is correct/safe | That the infra actually works |
| Requires knowledge of | Basic commands | Programming in Go |
That's why they are not run on every trivial change: since they create real resources (takes minutes and costs money), integration tests are usually reserved for important changes, to validate modules before publishing them, or are run periodically (for example, nightly), instead of on every small commit.
Do I need this to get started?
Not at first. The testing pyramid makes sense in order of cost/benefit:
▲ Few: integration tests (Terratest) — costly, for important things
╱ ╲
╱ ╲ Some: security analysis (Checkov/tfsec)
╱─────╲
╱ ╲ Many: fmt + validate — cheap, on every change
╱─────────╲Start with the base (fmt, validate), add security (Checkov/tfsec), and reserve integration tests for when your infrastructure is critical or you publish reusable modules that many will use. You don't need Terratest from day one, but it's good to know it exists and what problem it solves.
What you should remember
- Static analysis (fmt, validate, Checkov) reads the code but does not check that the infrastructure actually works; that's what integration tests are for.
- An integration test follows the cycle create → verify → destroy: sets up the real infra in a test account, checks that it works, and deletes it. Like building and driving a prototype, not just looking at the blueprints.
- Terratest is a Go library that automates that cycle (apply → checks → destroy), ensuring cleanup always happens (with
defer destroy), even if the test fails. - It allows you to verify real things: that a website responds, that a server is accessible, that the outputs are correct, that a module creates what it promises (ideal for validating modules).
- They are the most comprehensive tests but also the most costly (create real resources, take minutes, cost money, require Go); they are reserved for the important stuff, not for every trivial change. Start with the base of the pyramid.
In the last subsection of the chapter, we will see a technique to ensure that modules fit well together: contract testing between 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
