Executive Summary

Processing massive volumes of data reliably and at scale is no longer optional in today’s data-driven world.

This case study explores a serverless batch processing platform on AWS, capable of handling millions of records from large text files while maintaining reliability, visibility, and operational simplicity.

The Challenge

The system needed to:

Ingest large text files uploaded by users
Process each record independently
Provide real-time insight into job progress and failures

Constraints and pain points included:

Files containing millions of lines
Partial failures allowed but must not block the batch
Traditional batch systems struggled with scalability, long-running processes, and operational overhead

The solution had to scale instantly, fail gracefully, and recover automatically without constant infrastructure management.

The Approach: Event-Driven Serverless Architecture

We adopted a fully serverless architecture on AWS with managed services:

S3: File storage and ingestion events
Lambda: File validation and record-level processing
DynamoDB: Job and record metadata persistence
Kinesis: Real-time record streaming
SQS: Batch-level progress monitoring

Key design principles:

Clear separation between file validation, record processing, job monitoring, and user APIs
High parallelism for record-level processing
Fault isolation to ensure failures don’t block the batch

Architecture Diagram / Workflow

How the System Works

1. File Ingestion & Validation

Users upload files to an S3 bucket
Lambda validates files, renames them, and moves to success/error folders
A batch job entry is created in DynamoDB
Invalid files are blocked early, preventing downstream issues

2. Streaming & Batch Scheduling

Files processed line by line using Node.js streaming
Records pushed to Kinesis for real-time processing
Batch metadata sent to SQS for delayed job monitoring

3. Record-Level Processing

Kinesis-triggered Lambda processes each record independently
Downstream services invoked per record
Results persisted in DynamoDB
Failures isolated to individual records

4. Monitoring, Retries & Completion

SQS-triggered Lambda monitors job progress
Controlled retry with backoff for failed records
Batch marked complete when all records succeed or reach retry limits

5. Job Visibility & APIs

API Gateway + Lambda provide users with:
- Batch job status
- Record-level progress
No direct access to internal logs required

Key Challenges

Designing DynamoDB access patterns to avoid hot partitions
Managing Kinesis throughput under high load
Operating within Lambda concurrency and API limits
Implementing idempotent retries to avoid duplicate processing

Results

The system delivered:

Reliable processing of millions of records per batch
Automatic scaling with no infrastructure management
Fault isolation at the record level
Controlled retries with backoff
Clear visibility into batch and record-level status

The platform proved stable, cost-efficient, and adaptable to evolving data needs.

Key Takeaways

Event-driven, serverless architectures are highly effective for large-scale batch workloads
Combining managed cloud services, careful data modeling, and streaming techniques enables:
- Effortless scaling
- High reliability under load
- Operational simplicity

Building a Scalable Batch Processing Platform on AWS