In the previous subchapter, we saw why companies separate their workloads into many accounts. But that poses an immediate challenge: if you’re going to have dozens or hundreds of accounts, how do you create and configure all of them with the same security and organizational best practices, without going crazy doing it manually one by one? That’s what AWS Control Tower is for, which automates the creation of a well-configured multi-account environment (a landing zone), and its Account Factory, which manufactures new accounts with everything ready. It’s like having a factory for secure and compliant accounts.
The problem: setting up and maintaining many accounts by hand is unfeasible
Configuring one AWS account with all best practices (security, networks, logs, permissions, rules...) already takes work. Doing it for dozens or hundreds of accounts, and keeping them all consistent over time, is a nightmare if done manually:
By hand, for each new account: - configure security, networks, permissions... - apply company rules - connect centralized logs - ensure it meets standards → repeat this 100 times, without errors and keeping it up to date = impossible
You need to automate the creation and configuration of accounts, ensuring that all are born well-configured and follow the rules. That’s what Control Tower does.
What is a landing zone
Before Control Tower, a concept: a landing zone is a well-designed, secure, and ready-to-use multi-account environment that serves as a solid foundation on which the company deploys its applications. It’s the “foundation” prepared and compliant with best practices where your workloads “land.”
Landing zone = the prepared base of your organization in AWS: ├── well-organized account and OU structure (Ch. 23.1, 30.1) ├── security and rules (SCP) applied ├── centralized logs and auditing └── everything ready for teams to deploy on top with confidence
Analogy: a landing zone is like a housing development with all infrastructure already built before constructing the houses: the streets, water, electricity, sewage, and community rules are already prepared and compliant. When each neighbor arrives (each team), they only have to build their house on a solid, well-planned foundation, without worrying about the common infrastructure. Without the prepared development, everyone would improvise their own pipes: chaos.
What is AWS Control Tower
AWS Control Tower is a service that automatically creates and manages a landing zone with best practices: it sets up the multi-account structure, applies security rules, configures centralized logs and auditing, and gives you a dashboard to govern everything. Instead of building the landing zone by hand, Control Tower sets it up for you following AWS best practices.
AWS Control Tower: ├── automatically sets up the landing zone (structure + security + logs) ├── applies "guardrails" (security barriers) to all accounts ├── provides a central dashboard to view and govern the entire organization └── includes the Account Factory to easily create new accounts
Analogy: Control Tower is like the expert builder who prepares the entire development (the landing zone) following the rules, and also leaves you a system to add new developed lots when you need them. It saves you all the work of planning and building the foundation, and ensures it meets standards.
Guardrails (security barriers)
Control Tower applies guardrails: security and compliance rules imposed on accounts to keep them within allowed boundaries (many are implemented with SCP, see subchapter 23.1, and with Config, subchapter 23.2). For example, “no account can disable logs” or “operations are only allowed in certain regions.” They ensure that all accounts, no matter what, respect minimum standards.
What is Account Factory
The Account Factory is the part of Control Tower that allows you to create new accounts in an automated and standardized way. When a team needs a new account, instead of configuring it by hand, the Account Factory manufactures it already configured with all best practices, rules, and company connections applied from the start.
Team needs a new account
→ Account Factory creates it automatically, already with:
✓ security and guardrails applied
✓ connected to centralized logs
✓ following company standards
→ account ready to use in minutes, well configured from the startAnalogy: the Account Factory is like an assembly line that produces “turnkey” accounts. Just as a car factory produces identical vehicles that have passed all quality controls, the Account Factory produces new accounts all equally well configured and compliant, without anyone having to assemble them by hand. You request an account and it comes out ready to use, meeting all standards.
Why it matters: order and security at scale from day one
The great value of Control Tower and the Account Factory is that they allow companies to scale to many accounts while maintaining order, security, and compliance, without manual effort and without errors. Each account is well configured from birth and the entire organization is governed from a central dashboard. This links directly to the Well-Architected Framework (Chapter 27): it’s operating with operational excellence and security at scale.
Without Control Tower: 100 accounts configured by hand = chaos, errors, insecurity With Control Tower: 100 accounts manufactured equally and compliant = order and security
Real-world example: a growing company knows it will go from 5 to more than 50 accounts in a year (a new team or project usually needs its own accounts, subchapter 30.1). They implement AWS Control Tower, which sets up a solid landing zone: OU structure, security, centralized logs, and guardrails. From there, every time a team needs an account, they request it through the Account Factory and receive it in minutes, already configured with all company rules (security, allowed regions, connected logs). The platform team doesn’t have to configure anything by hand, and they have the guarantee that no account is left out of compliance. They scale to 50 accounts with the order and security of day one. Without this, it would have been unmanageable chaos.
What you should remember
- Creating and maintaining many accounts by hand, all consistent and with best practices, is unfeasible; you need to automate.
- A landing zone is a well-designed, secure, and ready-to-use multi-account environment, the solid foundation on which the company deploys. Like a housing development with all infrastructure already built before constructing the houses.
- AWS Control Tower automatically creates and manages a landing zone with best practices (structure, security, centralized logs) and applies guardrails (barriers with SCP and Config) to all accounts, governing them from a central dashboard. Like the expert builder who prepares the development.
- The Account Factory manufactures new standardized accounts, already configured with company rules from the start. Like an assembly line for “turnkey” accounts.
- Its value: scaling to many accounts with order, security, and compliance from day one, without manual effort or errors (operational excellence at scale).
In the next subchapter, we’ll see how to centralize log and security management across all these accounts.
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
