TripAdvisor Data Scraper
Overview
A high-throughput Python scraper that collects TripAdvisor listings, descriptions, and images at scale. Text is stored in PostgreSQL with categorized location tables, while images are uploaded to Dropbox. Rotating proxies enable parallel collection across many regions. Outcome: Built a continuously refreshed TripAdvisor dataset that the client uses for travel-content and location-intelligence products.
Architecture & Pipeline
flowchart LR
n0["TripAdvisorSource site"]
n1["Parallel ScrapersRotating proxies"]
n2["Listings + DescriptionsText extraction"]
n3["PostgreSQLCategorized location tables"]
n4["Image ExtractionPer listing"]
n5["DropboxImage storage"]
n0 --> n1
n1 --> n2
n2 --> n3
n3 --> n4
n4 --> n5
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step2 fill:#f0fdfa,stroke:#0d9488,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step2;
class n3 step2;
class n4 step3;
class n5 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- Parallel scraping with rotating proxies for large-scale extraction
- Categorized PostgreSQL schema for fast querying
- Image pipeline with automatic upload to Dropbox
- Scheduled incremental updates for freshness
- Robust error handling, retries, and run logs
- Tech Stack:** Python, Selenium, BeautifulSoup, PostgreSQL, Dropbox API