We close the observability chapter with two very popular tools in the open source world that AWS offers as managed services: Prometheus (for collecting and storing metrics) and Grafana (for visualizing them in beautiful dashboards). They are the de facto standard in many companies, especially with Kubernetes, and understanding what they are and why to use them in their managed version opens the door to a huge ecosystem.
The context: the open source observability ecosystem
In addition to CloudWatch (AWS's native tool), there is a widespread open source ecosystem for observability. Two of the most popular tools are:
- Prometheus: for collecting and storing metrics.
- Grafana: for visualizing those metrics (and others) in dashboards.
Many people use them together and they are almost a standard, especially in Kubernetes environments (remember EKS, subchapter 17.4). The problem: installing and maintaining them yourself is a lot of work (servers, updates, scaling, backups...). That's why AWS offers managed versions of both, where AWS takes care of all that operation (remember the idea of "managed service" we saw with RDS in Chapter 8).
What is Prometheus (and Managed Prometheus)
Prometheus is an open source system for collecting and storing metrics, very popular, especially in the world of containers and Kubernetes. It collects metrics from your applications and services and stores them in an optimized way for querying.
Amazon Managed Service for Prometheus is the managed version offered by AWS: you use Prometheus, but AWS takes care of the servers, scaling, availability, and maintenance. You focus on your metrics, not on operating the Prometheus infrastructure.
Your applications / Kubernetes
│ (emit metrics)
▼
Managed Prometheus (collects and stores the metrics)
│ AWS manages the servers, scaling, availability...
▼
ready to query and visualizeAnalogy: Prometheus is like a warehouse specialized in storing measurements (millions of numbers over time), very well organized to find them quickly. The managed version is like renting that warehouse with all the staff included: you put in and query the measurements, but you don't worry about maintaining the building, security, or expanding it when it gets full. AWS operates it for you.
What is Grafana (and Managed Grafana)
Grafana is an open source tool for visualizing data in very powerful, flexible, and attractive dashboards. It is famous for its spectacular graphs and for being able to combine data from many different sources in a single dashboard (from Prometheus, from CloudWatch, from databases...).
Amazon Managed Grafana is the managed version: AWS operates Grafana for you (servers, updates, scaling, security), and you just create and use your dashboards.
┌──────────── Grafana Dashboard ────────────┐ │ Data from Managed Prometheus + CloudWatch │ │ + database + other sources, TOGETHER │ │ 📊 powerful and customizable graphs │ └───────────────────────────────────────────────┘
Analogy: Grafana is like a professional control panel design studio: it takes data from anywhere and turns it into clear, beautiful, and highly configurable visual screens. The managed version is like hiring that studio "turnkey": you design your panels, but you don't maintain the premises or the equipment.
How Prometheus and Grafana work together
The classic combination is Prometheus collects, Grafana visualizes:
Applications → Managed Prometheus (collects and stores metrics)
│
▼
Managed Grafana (visualizes those metrics in dashboards)Prometheus is the "warehouse of numbers" and Grafana is the "pretty screen" that displays them. Together they form a complete and widely used observability solution in the industry.
Why use these managed versions?
The key question: if CloudWatch already exists, why use managed Prometheus and Grafana? Common reasons:
- Industry standard: Prometheus and Grafana are the de facto standard in many companies, especially with Kubernetes. If your team already knows them or your ecosystem uses them, it makes a lot of sense.
- Without the pain of operating them: you get these powerful open source tools without having to install or maintain them (AWS does it).
- Grafana's flexibility: Grafana can combine data from many sources (Prometheus, CloudWatch, other clouds, databases...) in a single dashboard, ideal for multi-cloud or hybrid environments.
- Portability: since they are standard tools, your investment in dashboards and configuration is portable (fits with the OpenTelemetry philosophy from subchapter 24.4: avoiding lock-in).
Real world example: a company running its applications on Kubernetes (EKS) already uses Prometheus and Grafana, as is common in that world. Instead of maintaining those systems themselves (with the operational work that entails), they adopt Managed Prometheus and Managed Grafana. They keep exactly the tools their team masters, their dashboards work the same, but now AWS takes care of keeping them available and scaled. Also, in Grafana they combine in a single dashboard the metrics from Prometheus and some from CloudWatch, having a unified view. The best of both worlds: standard tools they know, operated by AWS.
CloudWatch vs Prometheus/Grafana: which one?
It's not that one is better; it depends on the context:
| CloudWatch | Managed Prometheus + Grafana | |
|---|---|---|
| Origin | Native to AWS | Open source (industry standard) |
| Integration with AWS | Total and immediate | Good, but less "native" |
| Ideal if | You are focused on AWS and want the simplest option | You use Kubernetes, multi-cloud, or your team already masters these tools |
| Portability | Tied to AWS | High (standard tools) |
To start and if you only use AWS, CloudWatch is the most straightforward. If you come from the Kubernetes/open source world or work multi-cloud, managed Prometheus + Grafana fit better.
What you should remember
- There is a very widespread open source observability ecosystem; two key pieces are Prometheus (collects and stores metrics) and Grafana (visualizes them in dashboards), widely used together, especially with Kubernetes.
- AWS offers managed versions: Amazon Managed Service for Prometheus and Amazon Managed Grafana, where AWS operates the servers, scaling, and maintenance (like any managed service).
- Prometheus = "measurement warehouse" optimized; Grafana = "dashboard studio" that combines data from many sources into powerful graphs. Classic combination: Prometheus collects, Grafana visualizes.
- They are used because they are the industry standard (especially with Kubernetes), to avoid the pain of operating them, for Grafana's flexibility with multiple sources, and for their portability (no lock-in, in line with OpenTelemetry).
- CloudWatch is ideal if you focus on AWS and want simplicity; managed Prometheus + Grafana, if you use Kubernetes/multi-cloud or your team already knows them.
You have completed Chapter 24 and, with it, you master observability in AWS: logs, metrics, alarms, dashboards, distributed tracing, the OpenTelemetry standard, and the managed open source tools! In Chapter 25 we will tackle another crucial aspect of operating in the cloud: cost optimization.
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
