We close Part VII with Chapter 31: Platform Engineering and Internal Developer Platform, a very current discipline that represents the peak of infrastructure maturity. The idea: instead of each team of developers having to learn and configure all the infrastructure on their own, a specialized team builds an internal platform that provides it ready-made, easily and securely. We start with the fundamental concept of this discipline: golden paths, built on Terraform.
The problem: each team reinventing the wheel
Imagine a company with many development teams. Each one needs infrastructure: networks, databases, servers, pipelines... Without a common platform, each team has to:
Each team, on their own: - learn Terraform, AWS, networks, security in depth - configure their infrastructure from scratch - make security decisions (Will I do it right?) - keep everything up to date → lots of duplicated work, inconsistent decisions, security errors
This has serious problems: a lot of work is duplicated, each team does things differently (inconsistency), and developers—experts in their application, not necessarily in infrastructure—can make security or design mistakes. Plus, they get distracted from what really adds value: their product.
Analogy: it's as if in a company each employee had to build their own computer, install the operating system, and configure the network before being able to work. It would be a huge waste of time, everyone would do it differently, and many would do it wrong. The logical thing is for an IT team to prepare standard, ready-to-use, and secure computers, and for each employee to focus on their work. Platform Engineering applies that same idea to cloud infrastructure.
What is Platform Engineering
Platform Engineering is the discipline of building an internal platform that makes it easy for development teams to create and manage their infrastructure easily, quickly, and securely, without having to be experts in all the details. A platform team builds "tools and paths" that other teams use.
The goal: for developers to be able to self-serve the infrastructure they need (remember cloud self-service, subchapter 1.2) in a simple and secure way, focusing on their product instead of infrastructure details.
What is a golden path
The central piece of Platform Engineering is the golden path: a recommended, easy, and well-designed way to do something, which the platform team prepares for developers to follow. It's the "prepared path" that leads to a good result effortlessly and without risks.
Golden path = the prepared and recommended path:
"Need a database? Follow THIS golden path:
fill in these few details and you'll get a database
properly configured, secure, and following the standards."A golden path does not force (teams could go off it if they have a special need), but it's so easy and good that most are happy to follow it. It makes the right thing also the most convenient.
Analogy: a golden path is like a well-marked and paved trail on a mountain. You could climb cross-country (do it all yourself), but it's hard, risky, and easy to get lost. The golden trail is prepared, safe, and takes you straight to the top with minimal effort. Most take it because it's simply the best way to get there. The platform team "paves" those trails for common needs.
How golden paths are built: on Terraform
Here it connects with everything you know. Infrastructure golden paths are typically built on Terraform and modules (Chapter 18). The platform team creates well-designed, secure, and compliant modules that encapsulate best practices, and offers them to developers as golden paths:
Platform team creates expert Terraform modules:
module "standard-database" (secure, with backups, well configured)
module "standard-web-app" (with load balancing, autoscaling, logs...)
│
▼
Developers use them with just a few parameters
→ they get expert infrastructure without being expertsRemember modules (Chapter 18) and the idea of modules as an internal product (which we'll see in subchapter 31.4): the platform team acts as a product team whose "customers" are the other developers, and whose "product" is these golden paths. All the discipline of modules, versioning (subchapter 18.4), and testing (Chapter 21) you learned applies here to create quality golden paths.
Why it matters: speed, security, and consistency
Golden paths on Terraform provide three major benefits at once:
- Speed: developers get their infrastructure in minutes, without having to learn everything or configure it from scratch.
- Security and best practices "out of the box": since the path was designed by the expert team, the infrastructure comes out secure and well done by default, without the developer having to be a security expert.
- Consistency: all teams using the same golden path get coherent infrastructure, making it easier to maintain and govern everything.
Without golden paths: each team slow, inconsistent, at risk of errors With golden paths: everyone fast, secure, and consistent "out of the box"
Real-world example: a company with 15 development teams sets up a Platform Engineering team. This team creates golden paths on Terraform: for example, a "standard web service" module that, by just specifying the name and a few parameters, deploys an application with load balancing, autoscaling, logs, security, and backups already configured according to the company's best practices. Before, setting all this up took each team days (and sometimes they did it wrong). Now, a developer has it in minutes, without being an infrastructure expert, and it comes out secure and consistent with the rest. The 15 teams move faster, make fewer mistakes, and the company maintains control. Developers focus on their product, which is what adds value.
What you should remember
- Without a common platform, each team reinvents the wheel with infrastructure: duplicated work, inconsistency, and risk of security errors, distracting from their product. Like if each employee had to build their own computer.
- Platform Engineering is the discipline of building an internal platform that makes it easy for teams to create and manage their infrastructure easily, quickly, and securely, without being experts in everything (self-service).
- A golden path is the recommended, easy, and well-designed way to do something, prepared by the platform team. It doesn't force, but it's so good and convenient that most follow it: it makes the right thing also the easiest. Like a marked and paved trail to the top.
- Infrastructure golden paths are built on Terraform and modules (Ch. 18): the expert team creates secure and compliant modules that developers use with a few parameters.
- They provide speed (infrastructure in minutes), security and best practices out of the box, and consistency across teams.
In the next subchapter, we'll look at an AWS tool to offer this type of pre-approved resources in a self-service way: the Service Catalog.
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
