In the previous subchapter, we saw the golden paths: prepared and secure ways to create infrastructure so that developers can self-serve without being experts. AWS offers a concrete tool to implement that idea of a "catalog of pre-approved resources that teams can deploy themselves": the AWS Service Catalog. It's like creating an internal "store" of infrastructure products approved by the company, where developers "buy" (deploy) what they need with the guarantee that it meets the standards.
The problem: balancing freedom and control
A company faces a tension when giving its teams access to the cloud:
- If you give developers total freedom to create anything in AWS, they gain agility, but they can create insecure, expensive, or non-compliant resources.
- If you block everything and require every resource to be approved and created by a central team, there is control, but teams become slow (waiting for others to give them what they need), losing the agility of the cloud.
Total freedom → agile but risky (insecure, expensive, non-compliant resources) Total lockdown → safe but slow (teams wait, no autonomy) → we need a middle ground: CONTROLLED freedom
You need a middle ground: teams should be able to self-serve what they need, but only from a set of approved and secure options. That is the Service Catalog.
What is AWS Service Catalog
AWS Service Catalog allows you to create a catalog of approved infrastructure products that teams can deploy themselves, in a controlled way. The company defines what can be deployed (preconfigured and compliant products), and developers choose from that catalog and deploy it themselves, with the guarantee that it meets the standards.
Company defines the CATALOG (approved products):
├── "Standard database" (secure, with backups)
├── "Standard web server" (well configured)
└── "Data environment" (compliant with standards)
│
▼
Developers choose from the catalog and deploy THEMSELVES
→ self-service, but only from approved and secure optionsAnalogy: Service Catalog is like the approved purchasing catalog of a company. In a large company, you can't buy anything you want with company money freely; nor do you have to ask for permission for every pencil. There is a catalog of approved suppliers and products from which you can directly order what you need, knowing it has already been validated (price, quality, standards). Service Catalog does the same with infrastructure: an "internal store" of pre-approved resources that teams request themselves.
How it works
- The company defines the products (pre-approved)
The platform or governance team creates products: preconfigured, secure, and compliant infrastructure templates according to company standards. These products are usually built on infrastructure as code (often connecting with the Terraform modules from subchapter 31.1, or with native AWS templates). Each product encapsulates best practices.
- Organized into a catalog with permissions
The products are grouped into a catalog, and it is controlled which teams can see and deploy which products (with permissions, remember IAM, Chapter 7). Thus, each team only accesses what corresponds to them.
- Developers self-serve
A developer enters the catalog, chooses the product they need (for example, "standard database"), fills in a few parameters, and deploys it themselves. They get their infrastructure in minutes, without waiting for anyone and with the guarantee that it meets the standards (because the product was already approved).
Developer: "I need a database" → enters Service Catalog → chooses "standard database" → fills in 2-3 parameters → deploys → gets a secure and compliant DB, in minutes, without asking for permission
Why it matters: governed self-service
The great value of Service Catalog is achieving the balance between freedom and control: teams have autonomy to self-serve (agility), but within safe limits defined by the company (governance). It's "freedom within a framework":
Service Catalog = team autonomy + company control "you can deploy what you need, but only from what is approved"
This links directly to the golden paths (subchapter 31.1): Service Catalog is a way to offer those golden paths as concrete products that teams deploy. And it reinforces security and compliance (Chapter 23) without sacrificing speed.
Real-world example: a company wants its teams to be agile but without creating insecure resources. They set up an AWS Service Catalog with pre-approved products: "standard web application," "standard database," "compliant storage bucket," etc., all built following their best practices for security and cost. They give each team access to the products they need. Now, when a team needs a database, they deploy it themselves from the catalog in minutes, instead of opening a ticket and waiting days for the central team. And the company has the peace of mind that everything deployed is pre-approved and compliant. They have gained agility and maintained control: teams move fast, but on safe paths.
What you should remember
- There is a tension between giving teams total freedom (agile but risky) and locking everything down (safe but slow); the solution is controlled freedom.
- AWS Service Catalog allows you to create a catalog of approved infrastructure products that teams deploy themselves, in a controlled way. Like an internal store of pre-approved products for the company.
- It works in three steps: the company defines products (secure and compliant templates, often on Terraform), organizes them into a catalog with permissions, and developers self-serve (choose, fill in a few parameters, and deploy).
- Its value: governed self-service — team autonomy (agility) within safe limits defined by the company (control). "Freedom within a framework."
- It is a way to offer the golden paths (subchapter 31.1) as concrete products, reinforcing security and compliance without sacrificing speed.
In the next subchapter, we will see a very popular tool to give developers a unified "portal" from which to access all this: Backstage.
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
