We close Part V by bringing together everything we've learned into a complete automated workflow: a CI/CD pipeline for Terraform. We've seen the individual pieces (plan review in subchapter 12.5, testing in Chapter 21); now we join them in an automatic "assembly line" that goes from writing code to deployment. We'll do this with GitHub Actions, one of the most popular CI/CD tools.
What is a CI/CD pipeline
A pipeline is an automatic sequence of steps that runs when you change the code. Remember the two concepts:
- CI (Continuous Integration): automatically checking that the code is correct (we saw this in subchapter 21.1: fmt, validate, security...).
- CD (Continuous Delivery/Deployment): automatically taking the approved code to its destination (in our case, applying the infrastructure).
Analogy: a pipeline is like a factory assembly line. The product (your code) moves through stations: at one it's inspected, at another it's tested, at another it's assembled, and at the end it comes out finished. Each station does its part automatically, and if something fails at one, the line stops before the defect moves forward.
What is GitHub Actions
GitHub Actions is the CI/CD system integrated into GitHub. It lets you define pipelines in a file inside your repository, and they run automatically on events like opening a Pull Request or merging to the main branch. There are equivalent alternatives (GitLab CI, Jenkins, CircleCI...), but GitHub Actions is very popular and easy to start with; the concepts are the same in all of them.
The pipeline is defined in a YAML file inside .github/workflows/:
my-repository/
└── .github/
└── workflows/
└── terraform.yml ← this is where the pipeline is definedThe three stages of the basic pipeline
A basic Terraform pipeline has three stages, covering everything we've seen:
┌─ LINT ──────┐ ┌─ PLAN ──────────┐ ┌─ APPLY ─────────┐ │ fmt -check │ → │ terraform plan │ → │ terraform apply │ │ validate │ │ (in the PR, it │ │ (after approval │ │ security │ │ is reviewed) │ │ and merge) │ └──────────────┘ └──────────────────┘ └──────────────────┘
Stage 1: Lint (checks)
"Lint" means reviewing the code for problems. Here, the cheap checks from Chapter 21 are run: terraform fmt -check, terraform validate, and security analysis (Checkov/tfsec). If anything fails, the pipeline stops: there's no point in continuing with badly formatted, invalid, or insecure code.
Stage 2: Plan (preview)
terraform plan is run (subchapter 11.4) and its result is published in the Pull Request for a teammate to review (exactly the flow from subchapter 12.5). This is the key security stage: nothing is applied until you can see and approve what will change.
Stage 3: Apply (deployment)
Once the PR is approved and merged to the main branch, the pipeline runs terraform apply automatically, applying the reviewed changes. Since the plan was already reviewed, this apply is safe and controlled.
What it looks like, roughly
A simplified pipeline in GitHub Actions would look like this (you don't need to memorize the syntax, just understand the structure):
name: Terraform
on:
pull_request: # when opening a PR → lint and plan
push:
branches: [main] # when merging to main → apply
jobs:
terraform:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4 # downloads the code
- run: terraform fmt -check # 1. lint
- run: terraform init
- run: terraform validate
- run: terraform plan # 2. plan (in the PR)
- run: terraform apply -auto-approve # 3. apply (only on main)
if: github.ref == 'refs/heads/main'Notice the triggers (on):
- On a Pull Request (
pull_request): lint and plan are run (reviewed, not applied). - When merging to
main(pushtomain): apply is run (already reviewed).
This implements exactly the team flow from subchapter 12.5, but automated.
The big advantage: nobody applies from their laptop
Remember the principle from subchapter 12.5: in a team, nobody applies Terraform by hand. The pipeline enforces this automatically:
- Changes always go through the checks (they can't be skipped).
- The plan is always reviewed before applying.
- The apply is executed by the system, in a consistent and recorded way, not by a person from their machine (with their particular configuration and risk of error).
Without pipeline: everyone applies from their laptop → chaos, errors, no record With pipeline: everything goes through the automatic chain → consistent, safe, traceable
A note about credentials
For the pipeline to apply changes in AWS, it needs credentials. ⚠️ Never write them in the pipeline file (it would be like leaving the keys in the door). Use GitHub Actions secrets (a secure vault) or, even better, a secure connection without permanent keys (OIDC), applying the least privilege principle (Chapter 7): the pipeline should only have permissions for what it needs to manage. We'll go deeper into credential security in Chapter 23.
What you should remember
- A CI/CD pipeline is an automatic sequence of steps that runs when the code changes: CI checks that it's correct, CD deploys it. Like an assembly line.
- GitHub Actions defines pipelines in a YAML file in
.github/workflows/; there are equivalent alternatives (GitLab CI, Jenkins...), with the same concepts. - The basic Terraform pipeline has three stages: Lint (fmt, validate, security), Plan (published in the PR and reviewed), and Apply (after approval and merge).
- The triggers: in a PR you do lint + plan (review); when merging to main you do the apply (already reviewed). It's the flow from subchapter 12.5, automated.
- The big advantage: nobody applies from their laptop; everything goes through the automatic chain, in a consistent, safe, and traceable way.
- ⚠️ The pipeline credentials go in secrets (never in the file) and with least privilege.
In the next subchapter, we'll look at a tool specialized for this Terraform workflow: Atlantis, which takes infrastructure GitOps to another level.
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
