In the previous subchapter we saw SQS, where a message goes to one consumer who processes it. But sometimes you want the opposite: for the same message to reach many recipients at once. That's what SNS (Simple Notification Service) is for, AWS's notification service. It's the perfect complement to queues.
The problem: notifying several services at once
Imagine that an order is placed in a store, and when that happens you want several systems to find out at the same time:
- The billing service (to generate the invoice).
- The inventory service (to deduct the stock).
- The emails service (to notify the customer).
- The analytics service (for statistics).
With an SQS queue, the message would go to one consumer. Here we want the notification "there is a new order" to reach all at once. That's exactly what SNS does.
What is SNS: the publisher/subscriber model
SNS works with the publisher/subscriber (pub/sub) model:
- A publisher sends a message to a topic.
- All subscribers to that topic receive a copy of the message, at the same time.
┌─── SNS Topic "new-order" ───┐
Publisher ─────► │ │
(order service) └──────────────┬───────────────┘
┌──────────┬────┴─────┬──────────┐
▼ ▼ ▼ ▼
Billing Inventory Emails Analytics
(subscriber)(subscriber)(subscriber)(subscriber)- Topic: the "channel" to which messages are sent (e.g.
new-order). - Subscription: each recipient subscribes to the topic to receive its messages.
Analogy: SNS is like a broadcast list or a radio channel. The announcer (publisher) broadcasts a message once, and everyone subscribed to the channel receives it simultaneously. The announcer doesn't need to know how many listeners there are or who they are.
SNS vs SQS: the key difference
It's essential not to confuse them:
| SQS (queues) | SNS (notifications) | |
|---|---|---|
| Model | One message → one consumer processes it | One message → all subscribers receive it |
| Relationship | One to one | One to many |
| Messages | Wait in the queue until processed | Delivered instantly to subscribers |
| Metaphor | Task list (each task, someone does it) | Broadcast list (everyone receives the notice) |
In summary: SQS distributes work (one message, one worker); SNS broadcasts notifications (one message, everyone is informed).
Types of subscribers
An SNS topic can have many types of subscribers, making it very versatile:
- A Lambda function (to react to the event, Chapter 14).
- An SQS queue (very important! we'll see it in a moment).
- An email (SNS sends an email).
- An SMS (a text message to a mobile phone).
- An HTTP/HTTPS endpoint (notify another system via web).
The fan-out pattern: SNS + SQS together
Here is the most powerful combination, and one of the most used architectures in AWS: the fan-out pattern. It consists of placing an SNS topic that broadcasts to several SQS queues, one for each service.
┌─── SNS Topic "new-order" ───┐
Publisher ─────► │ │
└──────────┬──────────┬───────┘
▼ ▼
SQS Queue SQS Queue
billing inventory
│ │
▼ ▼
Consumer ConsumerWhy is it so good to combine them? Because it combines the best of both:
- SNS broadcasts the notification to all services at once (one to many).
- Each SQS queue gives its service the resilience and buffering we saw in subchapter 15.1: if the inventory service goes down, its messages wait in its queue without being lost, while the other services continue to function normally.
Real world example: in the store, "new order" is published to the SNS topic. It instantly reaches the billing, inventory, emails, and analytics queues. The analytics service is under maintenance (down), but its messages accumulate in its queue and will be processed when it comes back. Meanwhile, billing, inventory, and emails process theirs without issue. No order is lost and no service blocks the others.
This fan-out pattern is the basis of many event-driven architectures (we'll see it in subchapter 15.4 and in Chapter 28).
What you should remember
- SNS is the notification service with a publisher/subscriber model: a message sent to a topic reaches all its subscribers at once (one to many).
- Key difference with SQS: SQS distributes work (one message → one consumer); SNS broadcasts notifications (one message → all subscribers). Like a broadcast list or a radio channel.
- Subscribers can be Lambdas, SQS queues, emails, SMS, or HTTP endpoints.
- The fan-out pattern (SNS → several SQS queues) combines the best of both: broadcast to all services + resilience and buffering of each queue. It's one of the most used architectures.
In the next subchapter we'll see a more modern and flexible event service that expands on these ideas: EventBridge.
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
