We close Part VI with Chapter 26: High availability and disaster recovery, which deals with how to make your systems resist failures and disasters. Because things fail: a server goes down, a region has problems, someone deletes data by mistake. The question is not if something will fail, but when, and how prepared you are. Before looking at strategies and tools, we need two fundamental concepts that guide all recovery decisions: RTO and RPO.
The starting point: failures are inevitable
An uncomfortable truth about systems: everything fails at some point. Disks, servers, networks, even entire data centers. Serious companies don’t pretend it won’t happen; they prepare for when it does. This preparation for recovering from major failures is called disaster recovery (DR).
But “being prepared” costs money and effort, and not all applications need the same level. How much should you invest in recovery? To answer that, you first have to define what level of recovery you need, and that is measured with two questions: RTO and RPO.
RTO: how long can I be down?
RTO (Recovery Time Objective) is the maximum time your system can be down after a disaster before recovering. It answers the question: “if this goes down, how quickly do I need it back up?”
Disaster occurs System recovered
│ │
▼ ▼
├──────── RTO ─────────────┤
│ (downtime I can │
│ tolerate) │Examples of RTO by system type:
- An online store during a campaign: RTO of minutes (every minute down = lost sales).
- An internal reporting tool: RTO of hours (annoying, but tolerable).
- A historical archive system: RTO of days (almost no one notices).
Analogy: RTO is like asking yourself, if your car breaks down, “how long can I be without a car?”. If you need it for work every day, you want it fixed in hours (low RTO), even if that means paying for urgent towing and express repair. If it’s a weekend car, you can wait a week without a problem (high RTO) and look for the cheapest repair.
RPO: how much data can I afford to lose?
RPO (Recovery Point Objective) is the maximum amount of data (measured in time) you can afford to lose in a disaster. It answers: “if this goes down, up to what point in the past do I need to recover the data without it being a problem?”. In practice, it determines how often you need to make backups.
If your last backup was an hour ago and a disaster occurs, you lose the last hour of data. Examples:
- A bank: RPO of seconds (cannot lose a single transaction).
- An online store: RPO of minutes (losing a few minutes of orders would be serious but not catastrophic).
- A blog: RPO of hours or a day (losing the latest comments is tolerable).
Analogy: RPO is like asking yourself “how much work can I afford to lose if the computer shuts down without saving?”. If you save every 5 minutes, at most you lose 5 minutes of work (RPO of 5 min). If you only save once a day, you could lose a whole day’s work. The less you can afford to lose, the more often you must save (more frequent backups).
RTO and RPO together: two different questions
It’s essential not to confuse them: they measure different things.
┌──────────── DISASTER ────────────┐ │ │ RPO looks to the PAST RTO looks to the FUTURE "How much data do I lose?" "How long until I’m back?" (backup frequency) (recovery speed)
| RTO | RPO | |
|---|---|---|
| Measures | Tolerable downtime | Data you can lose |
| Question | How long until I’m back? | How much data do I lose? |
| Looks to | The future (recovery) | The past (last backup) |
| Affects | Recovery speed | Backup frequency |
Why they matter: they define your strategy (and your cost)
RTO and RPO are the compass for your entire recovery plan. The stricter they are (RTO and RPO of minutes or seconds), the more expensive the solution (you need duplicated systems, constant backups, automation...). The more relaxed, the cheaper.
Very low RTO/RPO (minutes/seconds) → expensive and complex solution High RTO/RPO (hours/days) → cheap and simple solution
That’s why the first step is always to ask the business: “how much downtime and how much data can we tolerate?”. The answer determines how much to invest. There’s no point spending a fortune on instant recovery for a system no one would miss for a day.
Real world example: a company defines RTO and RPO for each system. For its payment platform: RTO of 5 minutes and RPO of 0 (they can’t lose any transaction or be down), so they invest in a duplicated and costly architecture. For its internal reporting system: RTO of 8 hours and RPO of 24 hours, so a simple daily backup and manual recovery are enough, saving a lot of money. Same company, very different strategies, each tailored to what each system really needs. Defining RTO and RPO first lets them invest money where it really matters.
What you should remember
- Everything fails at some point; serious companies prepare to recover (disaster recovery). But “being prepared” costs, and each system needs a different level.
- RTO (Recovery Time Objective): the maximum downtime tolerable before recovery (“how quickly am I back?”). Looks to the future; affects recovery speed.
- RPO (Recovery Point Objective): the maximum amount of data (in time) you can lose (“how much data do I lose?”). Looks to the past; determines backup frequency.
- Don’t confuse them: RPO looks to the past (data lost), RTO looks to the future (time to return).
- The stricter (minutes/seconds), the more expensive the solution. That’s why the first step is to ask the business what it can tolerate, and invest accordingly.
In the next subchapter we’ll look at the different disaster recovery strategies (from the cheapest to the fastest) that you choose according to your RTO and RPO.
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
