Batch Processing Jobs for Import/Export of Structured and Semi-Structured Data

First published on March 01 2025 in Linkedin.

Client Overview

Our client, a leading India-based crop insurance service company, needed a robust and scalable solution to import and export survey data efficiently. Given the volume and complexity of their data, a manual approach was no longer feasible. They required an automated, serverless batch processing system that could handle structured and semi-structured data with minimal operational overhead.

Challenges & Requirements

The client faced several challenges in their data processing workflows:

✅ Handling Large Data Imports & Exports

• The client needed a system to seamlessly process Excel sheets, attachments, and survey data without manual intervention.
• The solution had to support structured (Excel, CSV) and semi-structured (JSON, XML) formats.

✅ Batch Job Management & Status Tracking

• The system had to enable tracking of batch processing status with detailed logs and notifications.
• Users needed real-time visibility into job execution progress.

✅ Optimized Cost & Scalability

• A cost-efficient, serverless approach was required to minimize infrastructure costs.
• The solution had to auto-scale based on data load fluctuations.

✅ Error Handling & Recovery

• A retry mechanism was needed for job failures to ensure high reliability.
• The system had to handle partial failures and recover gracefully.

✅ Security & Access Control

• Only authorized users should be able to initiate, monitor, and retrieve batch jobs.
• Secure storage of input/output files was essential.

Our Solution: AWS-Powered Batch Processing System

To address these challenges, we designed a serverless, event-driven architecture using AWS services. The key components included:

1️⃣ Batch Processing Orchestration with AWS Step Functions & Lambda

• AWS Step Functions coordinated the execution of batch jobs, ensuring efficient task management.
• AWS Lambda functions handled individual processing tasks, enabling parallel execution and faster performance.

2️⃣ Web Application Interface for Self-Service Job Submission

• A React.js-based Web UI enabled users to upload files, submit batch jobs, and monitor status in real-time.
• The interface reduced IT dependency, allowing business teams to manage their jobs independently.

3️⃣ AWS DynamoDB & Amazon S3 for Secure Storage

• Amazon S3 was used to store input data, intermediate results, and final processed files securely.
• AWS DynamoDB maintained job metadata, execution logs, and access control rules.

4️⃣ Scalable, Cost-Optimized Compute with Auto-Scaling

• The system was fully serverless, reducing infrastructure costs.
• DynamoDB auto-scaling ensured efficient handling of varying batch loads.

5️⃣ Advanced Error Handling & Retry Mechanism

• A retry logic with exponential backoff allowed automatic recovery from temporary failures.
• Partial failures were logged, and affected records were reprocessed, ensuring data integrity.

Results & Business Impact

📌 50% Cost Savings – By using AWS serverless services, the client significantly reduced operational costs.

📌 3X Faster Processing – Batch jobs that previously took hours were now completed in minutes.

📌 Scalability Without Downtime – The system scaled dynamically, handling high loads with zero disruptions.

📌 Improved Productivity – The self-service web interface eliminated manual processes, improving efficiency.

📌 Real-Time Tracking & Transparency – Users could track jobs, receive alerts, and download results instantly.

Technology Stack

✅ Frontend: React.js (for intuitive user interface)

✅ Backend & Processing: AWS Lambda, Step Functions, Python, Java

✅ Data Storage: Amazon S3 (secure storage), DynamoDB (metadata & logs)

✅ Authentication & Access Control: AWS Cognito, IAM Policies

✅ Job Scheduling & Orchestration: AWS Step Functions, EventBridge

Conclusion

By adopting a serverless, event-driven batch processing architecture, we helped our client automate large-scale data imports/exports, minimize costs, and improve operational efficiency. The solution empowered field agents with faster, error-free data processing, ensuring seamless crop insurance operations.