Application architecture patterns on AWS

1. Application architecture patterns on AWS

A great application can still fail in production if it was never shaped for the cloud. In this course, you'll learn to design, secure, and operate applications that deploy cleanly on AWS.

2. Meet your instructor

I'm Dunieski Otano, an AWS Solutions Architect with nearly a decade helping teams run real workloads on AWS. This course builds on "Developing Applications on AWS", so you already know the patterns; now we apply them to real deployments. Everything maps to the deployment portion of the AWS Certified Developer Associate exam, the DVA-C02. Let's get started.

3. Why the right architecture matters

Before we meet the patterns, here is why this choice matters so much. There is no single best architecture; each one trades simplicity for scale, or speed for resilience. The good news is that three questions guide almost every decision. How tightly are your components coupled? Where does the state live? And is the communication synchronous or asynchronous? Hold those three questions in mind, and the patterns that follow will feel like answers rather than a list to memorize.

4. The Friday night cascade

It's Friday night. The payment provider slows down, and within minutes the whole checkout freezes, because every service waited synchronously on the one that stalled. One slow dependency took down the store. The fix is an architecture that contains failure.

5. Why architecture choices decide deployment

That cascade is rarely about the code; it's the architecture pattern underneath. The pattern you pick shapes how an application scales and how it fails, and a tightly coupled design breaks under load. The right pattern, by contrast, makes deployment predictable: two teams can ship the same feature and get completely different production behavior from these choices alone. So before we reach for any specific service, we get the shape right.

6. The core architecture patterns

Three patterns cover most of what you'll deploy on AWS. A monolith is a single deployable unit, simple to build and a fine starting point. Microservices split the app into independent services you deploy and scale on their own. Event-driven architectures react to events instead of calling each other directly, which loosens coupling. Fanout is not a separate pattern but a common event-driven shape, where one event reaches many consumers at once. Each fits a different workload.

7. Stateful vs stateless

A stateless component keeps no client-specific data between requests, so any instance can handle any one. A stateful component, by contrast, remembers prior interactions, like an in-memory session, so requests must return to the same instance, which works until that instance dies. Statelessness is exactly what lets AWS add or remove instances freely behind a load balancer; that is horizontal scaling. So the cloud-native move is to keep compute stateless and push shared state into DynamoDB or ElastiCache, so scaling never loses data.

8. Tight vs loose coupling

With tight coupling, a service calls another and waits. If that downstream service is slow or down, the caller is stuck too, and the failure cascades. Loose coupling puts a buffer, usually an Amazon SQS queue, between them. The producer drops a message and moves on; the consumer picks it up when ready, so the producer keeps working even when the consumer is offline. A queue is the simplest tool for turning a fragile chain into a resilient one.

9. Synchronous vs asynchronous

Synchronous communication means the caller blocks until it gets a result, the right choice when the user needs the answer now, like an account balance. Asynchronous means the caller hands off the work and returns immediately, trusting it gets done later, which fits slow tasks, traffic bursts, and anything the user need not wait on, like a confirmation email. The mistake teams make is defaulting to synchronous everywhere, which is what makes systems brittle under load.

10. Resilience patterns for third-party calls

External APIs fail, so you design for it. Retry with exponential backoff means you retry, but you wait longer after each attempt, instead of hammering a struggling service. A circuit breaker goes further: after repeated failures it stops calling that service entirely for a cooldown period, giving it time to recover. A dead-letter queue catches messages that fail repeatedly, so one poison message doesn't block the whole queue. The key insight: blanket retries during an outage amplify it. You combine these patterns, you don't just retry harder.

11. Let's practice!

Time to put these patterns to work. Let's practice!

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