Case Study

ETL PedidosYa Dashboard

Analytics Platform / Backend dashboard

Restaurant operations analytics platform that ingests PedidosYa CSV/XLSX reports, normalizes sales data and exposes dashboard KPIs through FastAPI.

Repository More projects

Stack

PythonFastAPISQLiteCSV/XLSXLangGraph

Problem

Restaurant operators need a practical way to turn PedidosYa exports into normalized operational insight without relying on manual spreadsheet work.

Approach

The project ingests CSV/XLSX reports, validates and normalizes the data in a Python backend, then exposes KPIs and analytics through FastAPI endpoints and a dashboard interface.

Key Decisions

Keep ingestion explicit around real PedidosYa CSV formats.
Use SQLite as a lightweight local persistence layer for normalized reporting data.
Expose analytics through backend APIs instead of coupling the logic directly to the UI.

Tradeoffs

The public repository documents backend behavior and tests, but does not include committed screenshots suitable for portfolio thumbnails.
The project favors pragmatic local analytics infrastructure over heavier warehouse-style architecture.