You already know that the state is the Terraform inventory. The next question is: where is that inventory stored? By default, on your own computer. But that causes problems as soon as you work in a team. In this subchapter, we’ll see the difference between local and remote state, and how to configure the classic S3 + DynamoDB backend, one of the most important practices in professional Terraform.
Local State: The Starting Point
By default, Terraform saves the state in a terraform.tfstate file in the project folder on your computer. This is called local state.
To learn and experiment on your own, it’s fine. But it has serious problems when things get serious:
Problem 1: No Collaboration
If the state is on your laptop, your teammates don’t have it. Each person would have their own version of the state, which contradict each other. Impossible to work as a team.
Problem 2: Risk of Loss
If your laptop breaks, gets lost, or you accidentally delete the folder, you lose the state. And you already know (subchapter 11.2) how serious it is to lose the state: Terraform “forgets” everything it manages.
Problem 3: No Locking
If two people run apply at the same time on the same infrastructure, they could corrupt the state or overwrite each other’s changes. Local state has no way to prevent this.
Problem 4: Security
A file with sensitive data (subchapter 11.2) on your laptop, unencrypted, is a risk.
Remote State: The Professional Solution
Remote state saves the tfstate in a central and shared location (in the cloud), instead of on your computer. This solves all the previous problems:
- Collaboration: the whole team reads and writes the same central state.
- Security: it is stored encrypted and protected.
- Durability: it’s not lost if your laptop fails.
- Locking: prevents two people from modifying at the same time (we’ll see this soon).
The configuration of where the state is stored is called the backend. There are several types of remote backend; the most classic in AWS is S3 + DynamoDB.
The Classic Backend: S3 + DynamoDB
This combination uses two services you already know from Part II:
| Service | Role in the backend |
|---|---|
| S3 (Chapter 5) | Stores the state file (encrypted, versioned, durable) |
| DynamoDB (Chapter 8) | Manages locking so there aren’t two apply at the same time |
Analogy:
- S3 is the shared safe where the inventory (the state) is stored, accessible to the whole team, secure and backed up.
- DynamoDB is the “occupied/free” system of a bathroom: when someone is using the state (running
apply), they put up the “occupied” sign (a lock) so no one else enters until they’re done.
Why S3 is Ideal for State
- Durable (remember the “eleven nines” from Chapter 5): almost impossible to lose it.
- Encrypted at rest: protects sensitive data.
- Versioned (subchapter 5.3): keeps the history of the state, so you can recover a previous version if something goes wrong.
- Shared: the whole team accesses the same file.
Why DynamoDB for Locking
DynamoDB manages a “lock”: before modifying the state, Terraform puts a lock in a DynamoDB table. While that lock exists, no one else can run apply. When it finishes, it releases it. This prevents corruption from simultaneous access. We’ll see locking in depth in Chapter 20.
Note: Recent versions of Terraform/S3 allow managing locking directly in S3 without DynamoDB. But the S3 + DynamoDB pattern is still the most well-known and the one you’ll see in most projects and documentation.
How It’s Configured (Overview)
The backend configuration is declared in the terraform block:
terraform {
backend "s3" {
bucket = "my-company-terraform-state"
key = "production/network/terraform.tfstate"
region = "eu-west-1"
dynamodb_table = "terraform-locks"
encrypt = true
}
}bucket: the S3 bucket where the state is stored.key: the “path” inside the bucket (organizes states for different projects/environments).dynamodb_table: the table for locking.encrypt = true: encrypts the state.
After writing this, you run terraform init and Terraform configures the backend. We’ll see the details step by step in Chapter 20.
The Chicken and Egg Problem: to store the state in S3, you need an S3 bucket… but that bucket is also infrastructure. The usual solution: create the bucket and table once (by hand or with a small Terraform using local state) and then use them as the backend for everything else. We’ll see this in Chapter 20.
Local vs Remote: Comparison Table
| Local State | Remote State (S3 + DynamoDB) | |
|---|---|---|
| Where it lives | Your computer | In the cloud, shared |
| Team collaboration | No | Yes |
| Risk of loss | High | Low (durable, versioned) |
| Locking (prevents conflicts) | No | Yes (DynamoDB) |
| Encryption / security | Manual | Yes |
| When to use | Learning, solo testing | Any serious or team project |
What You Should Remember
- By default, the state is local (on your computer): fine for solo learning, but not for teams or production.
- Remote state stores it in a central and shared place (a backend), solving collaboration, security, durability, and locking.
- The classic backend in AWS is S3 + DynamoDB: S3 stores the state (encrypted, versioned, durable) and DynamoDB manages locking (prevents two simultaneous
apply). - It’s configured in the
terraform { backend "s3" { ... } }block and activated withterraform init. - For any serious or team project, use remote state. We’ll see it in detail in Chapter 20.
In the last subchapter of this chapter, we’ll review the essential Terraform commands you’ll use daily: init, plan, apply, destroy, fmt, and validate.
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
