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Supply Chain & Machine Learning • 2025The Vision & Architecture
Unifying 15.6 million retail transactions into
actionable vendor intelligence and ML features.
End-to-end data pipeline and analytics platform processing 15.6M+ transaction rows across 6 retail operational databases. Generates vendor financial telemetry, inventory turnover metrics, and an ML-ready feature store with 17+ engineered attributes for predictive modeling.
Core Capabilities
High-Throughput Multi-Table ETL
Unified 15.6 million rows across 6 tables (sales, purchases, pricing catalogs, inventory snapshots, and vendor freight invoices).
Vendor Financial Telemetry
Synthesized vendor performance across gross margins, freight-to-cost ratios, stock-to-sales velocity, and return on inventory.
ML Feature Store Engineering
Engineered 17+ predictive features including lead time variance, pricing power, and inventory turnover ready for Scikit-Learn pipelines.
Automated Data Quality Framework
Multi-stage validation asserting schema constraints, foreign key coherence, and outlier detection with automated JSON quality reports.
Memory & Performance Optimization
Chunked data loading and vectorized Pandas/SQL operations avoiding memory bottlenecks across multi-gigabyte transaction datasets.
Technology Stack
Architectural Decisions
Pre-aggregating vendor summaries vs on-demand queries
Pre-computing vendor metrics during ETL dramatically cuts dashboard response times and provides a structured feature store for ML.
Formal data quality verification step
Preventing corrupted or out-of-range purchase data from polluting downstream financial calculations and ML training sets.