In the previous subchapter, we saw disaster recovery strategies where, if the main system fails, traffic must switch to a backup system. But how do you detect that the main one has failed and redirect people automatically, without a human having to intervene at 3 a.m.? The answer combines two functions of Route 53 (the AWS DNS we saw in subchapter 16.1): health checks and failover.
Reminder: what Route 53 does
Remember from subchapter 16.1 that Route 53 is AWS's DNS service: it translates a domain name (like myshop.com) to the address of the server that should handle it. It's the first thing a user's browser checks to know where to connect. This gives it a privileged position: Route 53 decides where the traffic goes. And that's the key to automatic failover.
The problem: redirecting people when something fails
Imagine you have your main system in one region and a backup in another (as in the strategies from subchapter 26.2). If the main one goes down, you need users to stop going to the main (down) system and go to the backup (healthy) one. And you need this to happen:
- Automatically (without waiting for a human to notice and act).
- Quickly (every minute of downtime counts).
- In a reliable way (without sending people to a broken system).
For this, you first need to detect that the main one failed, and then redirect. Route 53 does both.
Health checks: monitoring if a system is healthy
A Route 53 health check is an automatic monitor that periodically checks if your system is responding correctly. Route 53 "asks" your system every so often: "are you okay?", and based on the response, marks it as healthy or unhealthy.
Route 53 every X seconds: "Main system, are you okay?" → responds correctly → HEALTHY ✓ (keeps sending traffic there) → doesn't respond / gives errors → UNHEALTHY ✗ (stops sending traffic there)
Analogy: a health check is like taking a patient's pulse every few minutes. As long as the pulse is normal, all is well. If the pulse stops or becomes abnormal, the alarm goes off and action is taken. Route 53 "takes the pulse" of your systems continuously to know which ones are alive and healthy.
The health check can check things like: does the website respond? Does it return a correct code? Does it respond in time? You define what "healthy" means.
Failover: switching to the backup automatically
Here's the magic. Failover is Route 53's ability to automatically redirect traffic from the main system to the backup when the health check detects that the main one is unhealthy.
Remember the routing policies from subchapter 16.1: one of them is precisely failover. You configure Route 53 like this:
Route 53 (failover policy): Main: region A (with health check) Backup: region B While A is HEALTHY → all traffic goes to A If A becomes UNHEALTHY → Route 53 AUTOMATICALLY redirects to B When A is HEALTHY again → traffic goes back to A
Normal operation: After A fails:
Users → [Region A ✓] Users → [Region A ✗]──╳
└──────────► [Region B ✓]Analogy: failover is like an emergency power generator in a hospital. As long as there's power from the grid (main system healthy), everything works normally. The instant the power goes out (main fails), a system automatically detects the outage and starts the generator (backup) in seconds, without anyone having to run and do it. The hospital keeps running without patients noticing. Health check = outage detector; failover = automatic generator startup.
How health checks and failover work together
The two are inseparable: the health check detects, the failover reacts:
HEALTH CHECK → monitors and detects that the main system went down
│
▼
FAILOVER → automatically redirects traffic to the backupWithout the health check, Route 53 wouldn't know something failed. Without failover, knowing it failed would be useless. Together, they achieve automatic traffic recovery, which is exactly what makes DR strategies (26.2) work without human intervention.
Real-world example: a company has its main website in the Ireland region and a backup (warm standby, subchapter 26.2) in Frankfurt, with Route 53 configured for failover. One night, the Ireland region has a problem and the website stops responding. The Route 53 health check detects it in seconds and marks Ireland as unhealthy. The failover automatically redirects all users to Frankfurt, which was ready. Customers barely notice a brief interruption. No one on the team had to wake up or do anything: the system recovered by itself. The next morning, when Ireland is restored, traffic automatically returns. That's resilience done right.
Beyond failover: geographic load balancing
These same capabilities (health checks + Route 53 routing policies) are also used to distribute users between regions by proximity (remember the geolocation and latency policies from subchapter 16.1), sending each user to the closest and healthy region. This way, system health is considered not only for emergencies, but also to provide the best service day to day.
What you should remember
- Route 53 (the AWS DNS, subchapter 16.1) decides where traffic goes, allowing it to manage automatic failover.
- A health check periodically checks if a system responds well and marks it as healthy or unhealthy. Like taking a patient's pulse continuously.
- Failover automatically redirects traffic from the main system to the backup when the health check detects the main is unhealthy (and returns it when it recovers). Like an emergency generator that starts by itself when the power goes out.
- They work together: the health check detects, the failover reacts. Together they achieve automatic traffic recovery, without human intervention, making DR strategies work.
- The same capabilities are used for geographic load balancing (sending each user to the closest and healthy region), not just for emergencies.
In the last subchapter of the chapter (and of Part VI) we'll see how to protect your data with centralized and automatic backups: AWS Backup.
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
