We begin Part VI: Advanced AWS, focused on the cross-cutting aspects that distinguish a professional: security, observability, and costs. And we start with defense in depth. The first concept is organizational and very powerful: how to manage multiple AWS accounts centrally with AWS Organizations, and how to set up security "guardrails" with Service Control Policies (SCPs).
The problem: a single account doesn't scale
So far, we've implicitly worked with a single AWS account. That's fine for learning, but real companies soon run into problems if they put everything into one account:
- No isolation: development and production share an account; a testing error can affect production.
- Hard to control costs by team or project (everything is mixed in one bill).
- Complicated permissions: it's hard to separate who can touch what.
- Concentrated risk: if the account is compromised, everything is at risk.
The professional solution is to use multiple accounts (one for production, another for development, another for each team...) and manage them centrally. We'll see the multi-account strategy in depth in Chapter 30; here we look at the basic tool.
What is AWS Organizations
AWS Organizations is the service that allows you to create and manage multiple AWS accounts centrally, grouped under an organization. You have a "root" (management) account, and from there you administer all the others.
Organization (management account)
│
┌───────────┼───────────┐
▼ ▼ ▼
Dev account Prod account Security accountMain benefits:
- Centralized management of all accounts from one place.
- Consolidated billing: a single bill for the entire organization (and discounts for aggregated volume).
- Centralized security control through SCPs (we'll see this in a moment).
Organizational Units (OU)
Within an organization, accounts are grouped into Organizational Units (OU), like folders. This allows you to apply rules to groups of accounts in an orderly way:
Organization
├── OU "Production"
│ ├── store-prod account
│ └── app-prod account
├── OU "Development"
│ ├── store-dev account
│ └── app-dev account
└── OU "Security"
└── audit accountAnalogy: an organization with OUs is like a company with departments. Management (the management account) sets general rules, and each department (OU) groups employees (accounts) to whom certain rules apply. You can give instructions to "the entire production department" at once.
What are Service Control Policies (SCP)
Here's the key security piece. A Service Control Policy (SCP) is a rule that defines the maximum limit of what can be done in an account or OU. They are like security guardrails: they set what is allowed at most, regardless of what individual permissions say.
SCP in the "Development" OU: "In these accounts, FORBIDDEN to create resources outside the eu-west-1 region" "In these accounts, FORBIDDEN to disable audit logs"
Important — the difference with IAM: remember IAM (Chapter 7), which grants permissions to users and roles. SCPs are different: they don't grant anything, but set a ceiling. Even if a user has admin IAM permissions, they cannot do something an SCP forbids. The SCP always wins.
Analogy: SCPs are like the laws of a country, and IAM like your work permissions. Your boss can authorize you to do many things (IAM), but no work authorization lets you break the law (SCP). The law sets the absolute limit that no one can cross.
Why SCPs are so powerful
SCPs allow you to impose non-negotiable rules on the entire organization, which no one can bypass, not even an account administrator. Typical examples of security guardrails:
- Restrict regions: "resources can only be created in Europe" (for data regulations, remember sovereignty from subchapter 3.4).
- Protect auditing: "no one can disable CloudTrail or security logs" (so there is always a trace).
- Forbid specific services: "in development accounts, expensive services X or Y cannot be used."
- Prevent dangerous actions: "no one can delete certain critical resources or encryption keys."
IAM grants permissions ───► ┌─ SCP (the ceiling) ─┐
│ what IAM │
EFFECTIVE permission = ──────►│ grants, but │
intersection of both │ limited by SCP │
└─────────────────────┘A person's real permission is the intersection: they can only do what IAM grants AND what the SCP allows. If either forbids it, they can't do it.
Real-world example: a company with European data protection requirements applies an SCP to the entire organization: "forbidden to create resources outside EU regions." From that moment, it doesn't matter if a developer has admin permissions: if they try to launch a server in the US, AWS will deny it. The compliance rule is technically guaranteed, not just in a written policy that someone could ignore.
The connection with defense in depth
SCPs are the outermost layer of your organization's security, the framework within which all others operate (IAM, Security Groups, WAF...). Remember defense in depth from subchapter 16.4: multiple reinforcing layers. SCPs set the maximum limits at the organization level; within them, IAM fine-tunes specific permissions, and other controls protect each resource.
What you should remember
- A single AWS account does not scale for companies (no isolation, mixed costs, concentrated risk); the solution is to use multiple accounts managed centrally.
- AWS Organizations allows you to create and manage multiple accounts centrally, with consolidated billing and grouped into Organizational Units (OU) (like departments).
- Service Control Policies (SCP) are security guardrails that define the maximum limit of what is allowed in an account or OU. Unlike IAM (which grants permissions), SCPs set a ceiling that no one can exceed, not even an administrator.
- Like the laws of a country versus your work permissions: no authorization lets you break the law.
- The real permission is the intersection of IAM and SCP. Typical uses: restrict regions, protect auditing, forbid services or dangerous actions.
- SCPs are the outermost layer of defense in depth at the organization level.
In the next subchapter, we'll see how to ensure your resources continuously comply with the rules using AWS Config.
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
