Data Scraping

Data scraping is the process of automatically extracting information from various sources, typically websites, documents, or other digital formats. This technique is essential for gathering large amounts of data that would be impractical to collect manually.

Common Scraping Methods

1. Web Scraping
2. HTML parsing
3. API consumption

4. Browser automation

5. Document Scraping

6. PDF extraction
7. Image text extraction (OCR)

8. Document format conversion

9. Database Scraping

10. Direct database queries
11. Export file processing
12. Log file analysis

Tools and Libraries

Web Scraping

• BeautifulSoup
• Scrapy
• Selenium
• Puppeteer

Document Scraping

• MinerU
• Apache Tika
• Tabula
• PyMuPDF

MinerU for Document Extraction

MinerU is a powerful open-source tool specifically designed for high-quality PDF extraction. It excels at: - Converting PDFs to machine-readable formats (Markdown, JSON) - Preserving document structure (headings, paragraphs, lists) - Extracting images, tables, and formulas - Supporting multiple languages through OCR - Handling complex layouts and scientific literature

Scraping Practices

1. Respect Rate Limits

   • Implement delays between requests
   • Follow robots.txt guidelines
   • Use appropriate request headers

2. Data Validation

   • Verify extracted data integrity
   • Handle missing or malformed data
   • Implement error logging

3. Performance Optimization

   • Use async operations when possible
   • Implement proper caching
   • Consider distributed scraping for large datasets