We close Chapter 26 (and Part VI) with the most fundamental piece of any disaster recovery strategy: backups. As we saw in subchapter 26.2, even the most basic strategy (Backup & Restore) relies on having good backups. But doing backups well, for many different services, reliably and automatically, is not trivial. To solve this, AWS offers AWS Backup: a service that centralizes and automates backups for your entire infrastructure.
The Problem: Scattered and Manual Backups
A typical company on AWS has data in many places: RDS databases (Chapter 8), server disks (EBS), files in EFS, data in DynamoDB... Each service has its own way of doing backups. Managing them separately is a mess and a risk:
❌ Not centralized: RDS → backups configured one way EBS → backups configured another way DynamoDB → backups configured another way EFS → did anyone remember to configure them? → easy to forget something, inconsistent, hard to audit
The problems with doing it this way: it's easy to forget to configure backups for some resource (and discover it when it's too late), each service is managed differently, it's hard to verify that everything is being backed up, and there's no common policy. You need a single place to manage all backups.
What is AWS Backup
AWS Backup is a service that centralizes and automates backups for many AWS services from one place. You define your backup rules once, and AWS Backup takes care of backing up all the indicated resources, automatically and consistently.
AWS BACKUP (one single place)
│
┌─────────┬───────┼───────┬─────────┐
▼ ▼ ▼ ▼ ▼
RDS EBS DynamoDB EFS others
(backs up all of them according to your rules, automatically)Analogy: AWS Backup is like hiring a professional backup service for your whole house, instead of each room taking care of itself. Instead of you remembering to back up your computer, your phone, your documents... a single service takes care of everything, according to a plan you define once, and guarantees that nothing is left without a backup. Centralized, automatic, and reliable.
What AWS Backup Does
- Centralized Backup Policies (Backup Plans)
You define backup plans that specify what to back up, how often, and how long to keep it, and they are applied automatically:
Backup plan: - What: all databases and disks with the "production" tag - How often: daily backup at 3:00 AM - Retention: keep backups for 30 days
Notice how this connects with RPO (subchapter 26.1): the frequency of backups (how often) determines how much data you can lose. If you back up every hour, your RPO is one hour.
- Apply Backups by Tags (at Scale)
AWS Backup can apply a plan to all resources with a certain tag (remember tags, subchapter 6.x). So, any new resource you create with the "production" tag is automatically included in the backups, without you having to remember. This avoids the error of forgetting to back something up.
- Lifecycle and Retention Management
It automatically manages how long backups are kept and when old ones are deleted (to avoid infinite costs), and can even move old backups to cheaper storage (remember storage classes, subchapter 5.x).
- Compliance and Auditing
From a single dashboard, you can see what is being backed up and what is not, making it easier to demonstrate (to auditors, for compliance) that your data is protected. This ties in with the compliance we saw in Chapter 23.
Why It Matters: Reliable Protection Without Forgetting
The great value of AWS Backup is the peace of mind of knowing that all your important data is backed up automatically, consistently, and verifiably, without depending on someone remembering to configure it resource by resource. Backups are your ultimate safety net: if everything else fails (accidental deletion, ransomware attack, catastrophic error), you can recover from the backups.
⚠️ A backup that hasn't been tested is not a backup. As important as making backups is verifying that they can be restored. Many companies discover their backups were corrupt or incomplete... just when they need them. Test your restores regularly.
Real-world example: a company suffers a ransomware attack that encrypts and damages data in several of its databases. Panic... until they remember they have AWS Backup with daily backups of all production resources (applied automatically by tag). They restore the databases to their state from the previous night. They lose, at most, a few hours of data (their RPO), but recover the business in hours instead of losing everything. The investment in centralized and automatic backups saved them. Without AWS Backup, maybe some critical database wouldn't have had a backup, with catastrophic consequences.
How This Closes Part VI
AWS Backup is the foundation on which disaster recovery strategies rely:
RTO and RPO (26.1) → define how much I can lose and how long it takes DR Strategies (26.2) → how I recover (all need backups) Route 53 failover (26.3) → automatically redirect traffic on failure AWS Backup (this) → the SAFETY NET: centralized and reliable backups
Without good backups, no recovery strategy works. AWS Backup ensures that this foundation is solid.
What You Should Remember
- Managing backups for each service separately is a mess and a risk (easy to forget something, inconsistent, hard to audit). You need to centralize.
- AWS Backup centralizes and automates backups for many services (RDS, EBS, DynamoDB, EFS...) from one place. Like a professional backup service for the whole house.
- Features: backup plans (what, how often, how long to keep), tag-based application (new resources are included automatically), lifecycle/retention management, and centralized auditing.
- The frequency of backups determines your RPO (subchapter 26.1): more frequent backups = less data lost.
- Provides the peace of mind of having all data automatically protected; backups are the ultimate safety net against deletions, attacks, or disasters.
- ⚠️ Test your restores: a backup that hasn't been tested is not reliable.
You have completed Chapter 26 and, with it, all of Part VI (Advanced AWS: security, observability, and costs)! You now master how to secure, observe, optimize, and protect your infrastructure. In Part VII we will take a leap into reference architectures and expert patterns, starting with the AWS Well-Architected Framework.
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
