Building Resilient Event-Driven Apps Using AWS Native Services

Introduction

In moving to the cloud, several teams deploy the lift-and-shift approach, where virtual machines are set up in the cloud by moving existing workloads. Although this method brings them to the cloud, it doesn’t generally use its features for more or less than elasticity, managed services, and event-driven architectures.

In this blog, we will revolutionize the show by comparing an event-driven application around AWS Step Functions and AWS Event Bridge.

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The Problem

Orchestrating a Multi-Step Onboarding Workflow

For instance, we can outline a SaaS platform as an example. These are the steps to be taken when a new user completes the sign-up process:

Create a user record in Amazon DynamoDB. Send a welcome email via SESProvision user-specific resources (e.g., an Amazon S3 folder). Notify internal systems via Slack or a webhook.

Each implies that a different service and possibly different teams are employed with several error-handling paths. How do we design a system that is both scalable and decoupled and, in terms of observability, capable of handling these situations?

The Solution: AWS EventBridge + AWS Step Functions

Key Concepts:

AWS EventBridge provides an event-driven, decoupled mechanism for the communication of services.
AWS Step Functions are state machines that model workflows; they handle retries, branching, parallel execution, etc.
Their combined usage will make the system more adaptable to changes and easier. It will also reduce the time needed to localize the fault, and it can be watched.

Step-by-Step Implementation

Emit the Event

Your AWS Lambda function behind the/signup API only throws an event into the AWS EventBridge.

import boto3
import json

client = boto3.client('events')

def lambda_handler(event, context):
    response = client.put_events(
        Entries=[
            {
                'Source': 'myapp.signup',
                'DetailType': 'UserSignup',
                'Detail': json.dumps({'user_id': '12345'}),
                'EventBusName': 'default'
            }
        ]
    )
    return {"statusCode": 200, "body": "Event sent."}

import boto3

import json

client = boto3.client('events')

def lambda_handler(event, context):

response = client.put_events(

Entries=[

{

'Source': 'myapp.signup',

'DetailType': 'UserSignup',

'Detail': json.dumps({'user_id': '12345'}),

'EventBusName': 'default'

}

]

)

return {"statusCode": 200, "body": "Event sent."}

{
  "source": ["myapp.signup"],
  "detail-type": ["UserSignup"]
}

{

"source": ["myapp.signup"],

"detail-type": ["UserSignup"]

}

This rule invokes an AWS Step Functions state machine, forwarding the user_id.

2. Build the State Machine

And here’s the same state machine defined in a simplified JSON format:

{
  "StartAt": "CreateUser",
  "States": {
    "CreateUser": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:region:acct:function:CreateUser",
      "Next": "SendEmail"
    },
    "SendEmail": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:region:acct:function:SendWelcomeEmail",
      "Next": "CreateS3Folder"
    },
    "CreateS3Folder": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:region:acct:function:CreateUserS3",
      "Next": "NotifySlack"
    },
    "NotifySlack": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:region:acct:function:NotifySlack",
      "End": true
    }
  }
}

{

"StartAt": "CreateUser",

"States": {

"CreateUser": {

"Type": "Task",

"Resource": "arn:aws:lambda:region:acct:function:CreateUser",

"Next": "SendEmail"

"SendEmail": {

"Type": "Task",

"Resource": "arn:aws:lambda:region:acct:function:SendWelcomeEmail",

"Next": "CreateS3Folder"

"CreateS3Folder": {

"Type": "Task",

"Resource": "arn:aws:lambda:region:acct:function:CreateUserS3",

"Next": "NotifySlack"

"NotifySlack": {

"Type": "Task",

"Resource": "arn:aws:lambda:region:acct:function:NotifySlack",

"End": true

}

Benefits

Decoupling

Services don’t talk to each other directly. You can add or remove consumers without changing publishers.

Observability

AWS Step Functions offers built-in monitoring and visual execution flows. Debugging becomes easier than tailing logs across services.

Resilience

AWS Step Functions manages retries and fallbacks. You can even add Catch blocks to handle failures gracefully.

Real-World Enhancements

Security: Use AWS IAM roles with least privilege for each function.
Auditability: Log all events in an Amazon S3 bucket or Amazon CloudWatch for compliance.
Multi-Tenancy: Add tenant IDs in events to isolate flows per user.

Conclusion

Migrating from legacy application architectures to event-driven workflows is not only about modernizing but also about maximizing the capabilities of cloud-native services. Using Amazon EventBridge with AWS Step Functions, we can construct modular, scalable, and observable systems with less glue code and more flexibility.

This model decouples logic between an application and a service, offers better operational visibility, and greatly decreases failure blast radius, while remaining serverless and cost-effective. So next time you’re automating something more than a single task, think workflow, not workaround.

Drop a query if you have any questions regarding AWS Step Functions and we will get back to you quickly.

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CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

FAQs

1. Why do we want to use AWS EventBridge and not call AWS Step Functions directly from AWS Lambda?

ANS: – By working with Amazon EventBridge, you create a loose-coupling of your services. If you have an AWS Lambda function call AWS Step Functions directly, you establish a close dependency between them. This can be difficult to scale or migrate as your architecture grows and develops, especially for microservices-based organisations where different teams own the services.

2. Will my AWS Step Functions state fail if a step in the state fails?

ANS: – AWS Step Functions are designed for fault tolerance. If a single state (task) fails, you can specify a Retry policy, which causes the task to automatically retry itself based on error type and number of attempts.

WRITTEN BY Rajveer Singh Chouhan

Rajveer Singh Chouhan works as a Research Associate at CloudThat. He has been learning and gaining practical experience in AWS and Azure. Rajveer is also passionate about continuously expanding his skill set and knowledge base by actively seeking opportunities to learn new skills. Rajveer regularly reads blogs and articles related to various programming languages, technologies, and industry trends to stay up to date with the latest developments in the field.