Most Kubernetes tutorials skip the hardest part — connecting to real databases. Here's how to close that gap.

Most Kubernetes tutorials teach you with SQLite or in-memory databases.

Then you get to production and realize:

→ Real apps use managed databases
→ Real apps need caching layers
→ Real apps have async messaging
→ Real apps span multiple data stores

Nobody warned you about this.

In my course, students build a retail store app with five AWS managed services:

🛒 AWS RDS MySQL — Product catalog storage
📦 AWS RDS PostgreSQL — Order management
🛍️ AWS DynamoDB — Shopping cart (NoSQL)
⚡ AWS ElastiCache — Checkout session caching
📨 AWS SQS — Async order processing

Five microservices. Five AWS managed services. One complete application.

🖥️ UI (Spring Boot) — Frontend serving the store
📦 Catalog (Go) — Product listings from MySQL
🛒 Cart (Java) — Shopping cart using DynamoDB
💳 Checkout (NodeJS) — Payment flow with Redis caching
📋 Orders (Java) — Order processing via SQS + PostgreSQL

Different languages. Different databases. One unified app.

"Why not just run databases in Kubernetes?"

You can. But should you?

Managing databases in K8s means:
→ You handle backups
→ You handle failover
→ You handle storage scaling
→ You wake up at 2 AM when it breaks

AWS managed services mean:
→ Automatic backups
→ Multi-AZ failover
→ One-click scaling
→ AWS wakes up at 2 AM

For most teams, managed services = better sleep.

This is what "production-grade" actually means.

Everything in the architecture diagram is provisioned with Terraform:

→ VPC and subnets
→ RDS MySQL + PostgreSQL instances
→ DynamoDB tables
→ ElastiCache Redis cluster
→ SQS queues
→ Security groups
→ IAM roles

One "terraform apply" creates the entire data layer.

Infrastructure as Code isn't optional anymore. It's expected.

→ Terraform to provision all AWS resources
→ Kubernetes secrets for database credentials
→ ClusterIP services for microservice communication
→ Connection pooling and retry patterns
→ Monitoring database performance

Not theory. Working code. Real AWS bills.

Here's the reality:

Running this full stack for a few hours of learning costs less than a coffee.

→ RDS db.t3.micro: ~$0.02/hour
→ ElastiCache t3.micro: ~$0.02/hour
→ DynamoDB: Pay per request (pennies)
→ SQS: First 1M requests free

The course teaches you to spin up, learn, and tear down. No surprise bills. Just real experience.

Tutorial apps: 1 container, SQLite, localhost
Production apps: 10+ services, managed databases, multi-AZ

Most engineers hit this gap on their first real project. This course closes it before you get there.

Sections to check out:

→ 14_RetailStore_Microservices_with_AWS_Data_Plane
→ 19_Helm_RetailStore_AWS_Dataplane

Each section has complete Terraform + Kubernetes manifests.

GitHub Repo: https://github.com/stacksimplify/devops-real-world-project-implementation-on-aws

Star the repo if you find it useful!

JOIN 2,555+ STUDENTS BUILDING PRODUCTION-READY SKILLS

What AWS service gave you the biggest "production surprise"? Hit reply and let me know, I read every response.