Business Challenge
A company needed a secure, scalable solution to ingest and process large volumes of JSON data from external clients. The existing manual processes were slow, error-prone, and could not handle high-volume batch uploads efficiently. Additionally, direct access to internal infrastructure was not an option due to security requirements.
Key challenges included:
- → Enabling large-scale batch ingestion of JSON records efficiently and reliably
- → Providing a secure API for external clients without exposing internal infrastructure
- → Automating data processing to reduce manual intervention and errors
- → Ensuring integration with downstream data storage and ETL pipelines
- → Maintaining scalability to handle fluctuating workloads
Solution Delivered
We designed and implemented a fully automated batch-oriented data ingestion and processing system. External clients can securely upload data using pre-signed URLs, simplifying integration while maintaining strict security standards.
The solution included:
- → API endpoints via AWS API Gateway to securely accept batch uploads
- → Serverless processing using AWS Lambdas to automate ETL tasks
- → AWS S3 for scalable and durable storage of raw data batches
- → Queue-based processing with AWS SQS to handle high-volume asynchronous workflows
- → User authentication and access management with AWS Cognito
- → Integration with AWS ECS and RDS for downstream processing and structured data storage
The system provides a robust, automated, and secure pipeline for data ingestion and processing, reducing operational overhead and ensuring reliable handling of large datasets.
Technologies Used
→ Cloud & Storage: AWS S3, AWS RDS
→ Serverless & Processing: AWS Lambdas, AWS ECS
→ Messaging & Queues: AWS SQS
→ API & Security: AWS API Gateway, AWS Cognito
→ Programming: Java
→ Skills & Deliverables: ETL Pipeline, Data Warehousing & ETL Software, Solution Architecture
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