Your VPC is now complete on the inside: subnets, internet gateways, and traffic rules. But networks rarely live in isolation: sometimes you need to connect your VPC with another VPC, or reach AWS services without going through the internet. That's what VPC Peering and endpoints are for. With this, we close the networking chapter.
The problem: VPCs are isolated by default
Remember: a VPC is a fenced and isolated plot. Two different VPCs (even if they're in the same account) cannot communicate with each other by default. That's good for security, but sometimes you need them to talk.
Example: Your company has a VPC for the development team and another for the data team. The development team needs to access a database that lives in the data VPC. How do you connect them privately and securely, without sending traffic out to the internet?
VPC Peering: connecting two VPCs
VPC Peering creates a direct private connection between two VPCs, so their resources can communicate as if they were on the same network, using private IP addresses and without going through the internet.
Analogy: It's like opening a private door between two neighboring plots. The inhabitants of both can go from one to the other directly, without going out to the public street. Only those you authorize can go through that door.
Key features:
- Traffic goes through the AWS private network, never through the internet. It's secure and fast.
- Works between VPCs in the same account or in different accounts, and even between different regions.
- You have to add routes in the route tables of both VPCs so they know how to reach each other (remember the route tables from subchapter 6.4).
⚠️ Important limitation — not transitive: If VPC A is connected to B, and B to C, that does not mean A can talk to C. Peering only connects the two direct endpoints. For a complex topology with many VPCs, connecting all of them to each other (peering) becomes a mess. That's what Transit Gateway is for (a "central router" that connects many VPCs together), which is the large-scale solution you'll see in big architectures (we'll mention it in Chapter 30).
VPC Endpoints: reaching AWS services privately
Here we solve a very common and often invisible problem. Imagine a server in a private subnet needs to access an S3 bucket.
The problem: S3 is a "public" AWS service (accessed via an internet URL). So, normally, that private server would have to go out to the internet through the NAT Gateway (subchapter 6.3) to reach S3. That means:
- Going through the NAT Gateway (which costs money per data).
- Traffic goes "to the internet" even though it's to another AWS service.
VPC Endpoints solve this. An endpoint creates a direct private connection between your VPC and an AWS service (like S3, DynamoDB, and many others), without going out to the internet and without going through the NAT Gateway.
Analogy: Instead of leaving your plot to the public street to go to a service that's "just around the corner," you open a direct private tunnel to that service. Safer, faster, and cheaper.
Advantages of endpoints:
- More secure: traffic never touches the internet.
- Cheaper: you avoid NAT Gateway costs for talking to AWS services.
- Better performance: direct route through AWS's internal network.
Two types of endpoints
| Type | How it works | For which services |
|---|---|---|
| Gateway Endpoint | Added as a route in your route table. Free. | Only S3 and DynamoDB |
| Interface Endpoint | Creates a private network interface inside your subnet. Has a cost. | Most other AWS services |
Very profitable practical tip: If you have servers in private subnets that access S3 or DynamoDB a lot, create a Gateway Endpoint (it's free) and you'll save the NAT Gateway costs for that traffic. It's one of the easiest and most effective cost optimizations out there.
Other ways to connect networks (overview)
To give you the full map, these are the ways to connect your VPC with the private outside world (no need to master them now, just know they exist):
| Service | What it's for |
|---|---|
| VPC Peering | Connect two VPCs to each other |
| VPC Endpoints | Reach AWS services privately |
| Transit Gateway | Connect many VPCs (and on-premise networks) through a central router |
| VPN Site-to-Site | Connect your VPC with your on-premise datacenter via an encrypted tunnel over the internet |
| Direct Connect | Connect your VPC with your datacenter via a dedicated physical line (fast and stable) |
The last two are the basis of the hybrid architectures we saw in Chapter 1: that's how the cloud and on-premise are connected.
What you should remember
- VPCs are isolated by default; for two to communicate you need VPC Peering (a "private door" between them).
- Peering is not transitive (A-B and B-C does not imply A-C); for many VPCs, Transit Gateway is used.
- VPC Endpoints connect your VPC with AWS services (like S3) privately, without going out to the internet or through the NAT Gateway: safer, faster, and cheaper.
- Gateway Endpoints (S3 and DynamoDB) are free: use them to save NAT costs.
- To connect with your on-premise datacenter: VPN (over the internet) or Direct Connect (dedicated line).
With this, you close Chapter 6 and now understand networking in AWS. In Chapter 7 we'll see who can do what in your account: identity and access with IAM, the pillar of security in AWS.
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
