Event-Driven Batch Processing with AWS Containers

A comprehensive guide to implementing scalable batch processing workloads using ECS and EKS with event-driven architectures.

Blog Post Outline

Part 1: Introduction

• What is batch processing and when to use it
• Traditional vs Event-driven batch processing
• AWS container services comparison (ECS vs EKS for batch workloads)

Part 2: Architecture Overview

Three implementation patterns covered:

1. Scheduled Batch Jobs - Time-based execution using EventBridge
2. Queue-Based Processing - Event-driven with SQS + Auto-scaling
3. Kubernetes Jobs - EKS CronJobs and one-time Jobs

Part 3: Implementation Examples

Example 1: ECS Scheduled Tasks with EventBridge

• Image processing pipeline triggered daily
• CloudWatch Events Rule → ECS Task
• Use case: Daily report generation

Example 2: SQS-Driven ECS Tasks with Auto-Scaling

• Message queue triggers container tasks
• Auto-scaling based on queue depth
• Use case: Video transcoding, data processing

Example 3: EKS Batch Jobs

• Kubernetes Jobs for one-time processing
• CronJobs for scheduled workloads
• Use case: ETL pipelines, ML training jobs

Part 4: Monitoring & Observability

• CloudWatch Container Insights
• Custom metrics for batch job tracking
• Dead letter queues for failed jobs
• Cost tracking and optimization

Part 5: Best Practices

• Error handling and retries
• Idempotency patterns
• Resource optimization
• Security considerations

Architecture Diagrams

Pattern 1: Scheduled Batch with EventBridge

EventBridge Rule (cron) → ECS Task Definition → Fargate Task
                                ↓
                          CloudWatch Logs
                                ↓
                          SNS (Success/Failure)

Pattern 2: Queue-Based Processing

Event Source → SQS Queue → ECS Service (Auto-scaling)
                  ↓              ↓
            CloudWatch      Fargate Tasks
            (Queue Depth)        ↓
                           S3/Database
                                ↓
                           DLQ (Failed)

Pattern 3: EKS Jobs

EventBridge/Manual → Kubernetes Job/CronJob
                            ↓
                      EKS Worker Nodes
                            ↓
                    CloudWatch Logs/Metrics

Project Structure

.
├── README.md
├── ecs-scheduled/
│   ├── terraform/
│   ├── docker/
│   └── README.md
├── ecs-sqs-autoscaling/
│   ├── terraform/
│   ├── docker/
│   ├── lambda/ (SQS producer)
│   └── README.md
├── eks-jobs/
│   ├── terraform/
│   ├── k8s-manifests/
│   ├── docker/
│   └── README.md
└── monitoring/
    ├── cloudwatch-dashboards/
    └── alarms/

Technologies Used

• AWS Services: ECS, EKS, EventBridge, SQS, ECR, CloudWatch, SNS
• IaC: Terraform
• Container Runtime: Docker
• Languages: Python (sample applications), HCL (Terraform)

Prerequisites

• AWS CLI configured
• Docker installed
• Terraform >= 1.0
• kubectl (for EKS examples)
• eksctl (for EKS cluster setup)

Getting Started

Each subdirectory contains a complete working example with:

• Docker application code
• Infrastructure as Code (Terraform)
• Deployment instructions
• Testing and validation steps

Cost Estimation

Approximate monthly costs for running these examples:

• ECS Scheduled (1 task/day, 5 min): ~$1-2
• ECS SQS Auto-scaling (100 tasks/day): ~$10-20
• EKS Jobs (t3.medium nodes): ~$30-50

Key Takeaways for Blog Post

1. When to use ECS vs EKS for batch:

   • ECS: Simpler, serverless with Fargate, great for straightforward batch jobs
   • EKS: More complex workloads, need advanced scheduling, existing K8s investment

2. Event-driven benefits:

   • Cost efficiency (pay only when processing)
   • Automatic scaling based on demand
   • Loose coupling between services

3. Production considerations:

   • Implement idempotency for retries
   • Use DLQ for failed messages
   • Monitor queue age and task duration
   • Set appropriate timeouts and resource limits

Blog Post Flow

1. Hook: Start with a real-world problem (e.g., "Processing millions of images uploaded by users")
2. Context: Explain why containers are perfect for batch processing
3. Deep Dive: Walk through each implementation with code
4. Comparison: Side-by-side comparison of the three approaches
5. Production Tips: Share lessons learned and best practices
6. Call to Action: Encourage readers to try the examples and share feedback

Additional Resources

• AWS ECS Best Practices
• Kubernetes Jobs Documentation
• Event-Driven Architecture Patterns

License

MIT License - Feel free to use this code for your projects and learning