🚀 Streamlining Infrastructure Management and Enhancing End User Geolocation with AWS ECS, Lambda, and CloudFront

In this article, I’ll walk you through the architecture and implementation details of an application hosted on AWS ECS (using the Fargate launch type). We’ll explore how to build a CI/CD pipeline with AWS services like CodePipeline and CodeBuild, containerize our application, and add a geolocation feature using AWS Lambda, CloudFront, and S3. Let’s dive in!

1️⃣ Introduction

Managing infrastructure at scale can be challenging. Our goal was to:
✅ Simplify deployment using a serverless container approach.
✅ Automate the entire CI/CD pipeline.
✅ Enhance the application by identifying the geolocation of end users using CloudFront logs and Lambda.

2️⃣ Architecture Overview

Here’s a high-level look at the architecture:

• GitHub Repository: Stores the source code.
• AWS CodePipeline: Manages the CI/CD workflow.
• AWS ECR: Hosts Docker images.
• AWS ECS (Fargate): Runs containerized applications in a serverless manner.
• AWS Secrets Manager: Manages credentials securely.
• AWS CloudFront: Serves the application with low latency.
• AWS Lambda: Extracts and processes geolocation data.
• Amazon S3: Stores CloudFront logs, build artifacts, and geolocation data.

3️⃣ CI/CD Workflow

Here’s how we automated the deployment pipeline:

🛠️ Build Stage:

• CodePipeline detects changes in GitHub.

• It triggers CodeBuild to:

  • Fetch the latest code.
  • Build a Docker image.
  • Push the image to ECR using credentials from Secrets Manager.

🚀 Deploy Stage:

• Upon a successful build, CodePipeline triggers the deploy stage.
• ECS (Fargate) is updated with the latest Docker image.
• ECS handles deployment seamlessly.

4️⃣ Geolocation Workflow

Here’s how we identify the geolocation of users:

• CloudFront logs user request headers to S3.

• An event trigger invokes AWS Lambda (Python).

  • Lambda parses IP addresses from logs.
  • Lambda queries a geolocation API for details.
  • Lambda performs MapReduce on the data (aggregating results).
  • The final geolocation data is stored in S3.

5️⃣ Implementation Details

Here’s a quick rundown on setup:

• GitHub: Configure branches and CI/CD triggers.
• CodePipeline: Define stages, source provider (GitHub), build settings (Docker), and deployment to ECS.
• ECR: Create a repository to store Docker images.
• ECS: Setup a Fargate cluster and service.
• Secrets Manager: Store credentials securely.
• IAM Roles: Allow CodePipeline to interact with AWS services.
• CloudFront: Distribute content with low latency.
• Lambda: Python script to extract and store geolocation.
• S3: Store build artifacts and logs.

6️⃣ Benefits

✅ Serverless Deployment: No need to manage EC2 instances - thanks to Fargate.
✅ Automation: CI/CD reduces manual steps and errors.
✅ Scalability: Fargate and Lambda scale automatically.
✅ Security: Secrets Manager secures credentials.
✅ Low-Latency Content Delivery: CloudFront caches content at edge locations.
✅ Serverless Execution: Lambda runs code on-demand without servers.
✅ Object Storage: S3 is durable and scalable, perfect for logs and data.

7️⃣ Conclusion

This article shows how to effectively deploy an application on AWS ECS Fargate while enhancing user experience with geolocation tracking. Using services like CodePipeline, ECR, ECS, Secrets Manager, Lambda, CloudFront, and S3, we’ve built a secure, scalable, and automated deployment pipeline.

Here is the github repositort url for the application.

8️⃣ Future Plans

🔧 Implement Infrastructure as Code with AWS CloudFormation or Terraform.
🌍 Develop a custom geolocation API for advanced use cases.
📈 Add monitoring and alerting with AWS CloudWatch or third-party tools.
🌐 Enable cross-region replication for S3 to ensure data durability and disaster recovery.