Multi-Site Web Scraper

TL;DR

A Python-based data extraction pipeline that provides a unified interface for scraping structured data from six different platforms: Reddit, Quora, Hotmart, Mercado Libre, Amazon, and Instagram. Each platform adapter handles its specific anti-scraping measures and data formats while presenting results through a consistent output schema.

Problem

Data collection from web platforms is fragmented. Each site has different page structures, authentication requirements, rate limits, and anti-bot measures. Researchers and analysts who need data from multiple sources end up maintaining separate scrapers for each platform, duplicating common infrastructure like proxy rotation, request retry logic, and output formatting. The goal was to create a single tool that abstracts these differences behind a unified interface.

Role and scope

Solo project. I designed and implemented the complete system, including all platform adapters, the scheduling infrastructure, and output formatting. The scope covers extracting structured data (posts, products, comments, user profiles) from the six target platforms and exporting it in standard formats.

Constraints

• Platform heterogeneity: Each site has unique HTML structure, API endpoints (if available), authentication requirements, and anti-scraping measures.
• Legal and ethical: The tool must respect robots.txt, rate limits, and terms of service. It is designed for personal/educational use, not for commercial data harvesting.
• Reliability: Scrapers break when sites update their markup. The architecture must make it easy to fix individual platform adapters without affecting others.
• Data consistency: Output from different platforms must share a common schema so downstream consumers (analysis scripts, databases) can work with uniform data.

Architecture and decisions

The pipeline follows a plugin-adapter architecture:

• Core engine: Handles HTTP requests, response parsing, retry logic, proxy rotation, and output serialization.
• Platform adapters: Each platform has a dedicated adapter that implements extraction logic specific to that site. Adapters inherit from a common base class that defines the contract (methods like search(), get_details(), extract_comments()).
• Output layer: All adapters return data in a normalized schema, serialized as JSON or CSV.

This design was chosen over a monolithic scraper because:

• Platform-specific code is isolated – a Reddit API change only touches the Reddit adapter
• Adding a new platform means writing one new adapter class
• The core engine handles cross-cutting concerns (rate limiting, error handling, logging) in one place

Implementation

[Details to be added on specific libraries, extraction techniques, and code structure.]

Key implementation areas include:

• HTTP client with configurable headers, cookie handling, and proxy rotation
• Pagination handling for sites that load data through infinite scroll or page URLs
• Authentication adapters for platforms that require login (Instagram, Quora)
• Data normalization to map each platform’s native format to the unified schema

Reliability, security, and evaluation

The scraper uses exponential backoff for retries and configurable delays between requests to avoid overwhelming target servers. Credentials for authenticated platforms are managed through environment variables.

[Details to be added on specific reliability metrics, failure rates, and data accuracy evaluations.]

Result

The pipeline successfully extracts structured data from all six platforms through a single command-line interface. The plugin architecture has proven maintainable – when one platform updates its markup, the fix is isolated to that adapter without risking breakage in others. The unified output schema makes it straightforward to aggregate and analyze data from multiple sources.

Trade-offs and lessons

• Adapter pattern vs. shared logic: The adapter pattern isolates platform-specific code well, but there is meaningful shared logic (HTML parsing helpers, date normalization) that must be carefully extracted to avoid duplication.
• Authenticated vs. public scraping: Platforms with authentication (Instagram, Quora) are significantly more fragile. Session management adds complexity and a point of failure that public scraping avoids.
• Maintenance cost: Scrapers are inherently high-maintenance. Each platform update potentially breaks an adapter. The project is most valuable when data collection is a regular need, not a one-off task.
• Rate limiting design: Being too aggressive with requests causes blocks; being too conservative makes collection slow. Adaptive rate limiting that responds to server responses is the right approach for production use.