Enterprise Data Processing Platform

Overview

A local Python data-processing system with a CLI and authenticated FastAPI interface for validating synthetic order CSVs, storing accepted records, explaining rejections, and producing repeatable reports.

Independent portfolio project by Anup Kumar Dagala, built with AI assistance and synthetic data.

Problem

Make incoming order-file quality, duplicate handling, persistence, and operational reporting explicit and reproducible in a local system.

Implementation

A typed Python package shares validation, processing, storage, and reporting services across argparse and FastAPI interfaces, with locked dependencies, automated tests, CI, and a non-root container definition.

• CLI and authenticated local REST API
• CSV structure and record validation
• Atomic SQLite writes and duplicate handling
• Readable rejection records
• JSON and spreadsheet-safe CSV exports
• Deterministic synthetic demo and bounded benchmark

Security and reliability

• Shared-key authentication on business routes
• Parameterized SQL and transactional integrity
• Request, row, concurrency, rate, and retained-record limits
• Generated storage paths and spreadsheet-formula neutralization
• Documented threat model and CI security checks

Limitations

• A local learning demonstration using synthetic data, not a production SaaS or company deployment.
• One shared key, one process, one SQLite writer, and no user roles or distributed rate limiter.
• The recorded benchmark is ingestion-only on one laptop and is not a throughput target or SLA.
• Public deployment would require production identity, TLS, retention, backup, monitoring, and operational controls.