Hotel property ETL: resolve source-of-truth pages → clean structured data
A scraping + ETL system that resolves official property pages, extracts room/amenity/media data, and loads clean records into MySQL.
Problem
Hotel data was inconsistent across brands and sources, making downstream cataloging and analysis unreliable.
Stack
- - Python
- - MySQL
- - ETL
- - Browser automation
- - Data validation
Architecture
ETL architecture
A simplified view of how the system is wired. (Details and metrics are intentionally omitted until confirmed.)
- →
Step 01
Brand/source inputs
Seed URLs + discovery
- →
Step 02
Resolve canonical pages
Official property pages
- →
Step 03
Extract
Rooms, amenities, images
- →
Step 04
Transform
Normalize schema + validate
- →
Step 05
Load
MySQL tables for querying
- - TODO: Add real dataset size + run cadence once confirmed.
- - TODO: Add Upwork review excerpt + source link when available.
Outcome
What changed
- Consistent structured output across multiple brands
- Cleaner downstream queries via validation and normalization
- Repeatable pipeline designed for reruns and incremental updates
Metrics
Metrics are shown only when verified. Placeholders live in `lib/data.ts`.
Media
Demo / screenshots
Pipeline demo (click to load).
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