In the previous subchapter, we looked at official resources (documentation and Skill Builder), perfect for accurate information and structured training. But not all learning has to be “studying.” There are more lightweight, enjoyable, and comfortable ways to stay up to date and keep learning almost without realizing it: YouTube and podcasts. These are informal but extremely valuable resources that fit into the gaps in your day and keep you naturally connected to the cloud world.
Learning isn’t always “sitting down to study”
There is formal learning (courses, documentation) and informal learning, which happens while you watch an interesting video or listen to experts chat. This second type is more relaxed but just as useful: it gives you ideas, context, news, and tips without the effort of formal study. And, above all, it fits into your life without needing dedicated time blocks.
Formal learning: courses, documentation (you sit down to study) Informal learning: videos, podcasts (you learn in a relaxed way) → Both add up! Informal learning complements formal learning very well.
YouTube: learning by watching
YouTube is full of excellent (and free) content about AWS and the cloud: service explanations, step-by-step tutorials, demos, conference talks... Seeing someone do something or visually explain a concept can clarify things that are harder to grasp in text.
On YouTube you’ll find: ✓ Step-by-step tutorials (see how something is done) ✓ Visual explanations of concepts ✓ Conference talks (like AWS re:Invent, their big annual event) ✓ News and analysis of new services
💡 The official AWS channel on YouTube publishes tons of content, including re:Invent talks (AWS’s big annual conference, where the most important news is announced). There are also great independent educators who explain AWS in a very didactic way.
Analogy: YouTube is like having access to infinite free master classes from teachers all over the world. Some are gentle introductions, others are advanced expert talks; you can choose what you need at any moment, pause, repeat... It’s like an open, free university, available whenever you want.
Podcasts: learning by listening
Podcasts are audio shows (like on-demand radio programs) about technology, cloud, AWS... Their big advantage is that you can listen to them while doing other things: walking, commuting, exercising, cooking. They turn dead time into learning time, with no extra effort.
Podcasts: you learn by LISTENING, while doing other things ✓ While walking, driving, at the gym, cooking... ✓ Turn dead time into learning ✓ Keep you up to date with news and trends ✓ Hear experts share real experiences
They’re ideal for:
- Staying up to date with news and trends effortlessly.
- Learning from the experience of professionals who share how they solve real problems.
- Making use of times when you couldn’t read or watch a video.
💡 There are official AWS podcasts and many independent ones about the cloud and DevOps, in English and also in Spanish. Look for some, try several, and stick with the ones that give you the most and best fit your level.
How to make the most of them
- Want to SEE how something is done or understand visually? → YouTube - Want to learn during dead time (without a screen)? → Podcasts - Want to catch up on important news? → re:Invent talks (YouTube) and podcasts
⚠️ A balance tip: these resources are great for staying up to date and discovering things, but remember that the best way to learn is by building (like the projects in Chapter 33!). Don’t fall into just “consuming” content without ever practicing. The ideal balance: learn and get inspired with videos and podcasts, and then apply it by building your own projects.
Real-world example: someone with a job and little free time wants to keep growing in the cloud after the book, but finds it hard to carve out time to “sit down and study.” They find the solution in informal learning: they listen to AWS podcasts during their daily commutes (so, with no extra effort, they stay up to date with news and learn from the experiences shared by experts), and on weekends they watch YouTube videos on topics that interest them or tutorials on things they want to build. Without dedicating formal study blocks, they accumulate knowledge and stay connected to the industry. When something especially inspires them, they put it into practice with a small project. That combination of constant informal learning and occasional practice allows them to keep growing despite their lack of time.
What you should remember
- Not all learning is “sitting down to study”: informal learning (videos, podcasts) is relaxed but very valuable, and complements formal learning. It fits into your life without needing dedicated time blocks.
- YouTube: free and excellent content about AWS (tutorials, visual explanations, re:Invent talks, the big annual event). Like having infinite free master classes. 💡 The official AWS channel and good independent educators.
- Podcasts: you learn by listening while doing other things (walking, commuting, gym...), turning dead time into learning. Ideal for staying up to date and learning from experts’ experience. Available in English and Spanish.
- ⚠️ They’re great for staying up to date, but the best way to learn is by building (Ch. 33): don’t just “consume” content, apply it with your projects.
In the next subchapter, we’ll look at a resource that offers something videos and podcasts can’t: connecting with other people. We’ll talk about communities.
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
