Data Extraction Techniques

What is Data Extraction?

Data extraction is the process of retrieving structured or unstructured data from various sources for storage, analysis, or processing.

Common Data Sources

Data Extraction Methods

1. Web Scraping

Extracting data from HTML pages using parsers (Beautiful Soup, Cheerio).

Pros: Works on any public website Cons: Breaks when site structure changes

2. API Access

Fetching data from structured endpoints (REST, GraphQL).

Pros: Reliable, structured, official Cons: Not all sites provide APIs

3. Browser Automation

Using headless browsers (Puppeteer, Selenium) for JavaScript-heavy sites.

Pros: Handles dynamic content Cons: Slower, resource-intensive

4. Document Parsing

Extracting text/tables from PDFs, Word docs, Excel.

Pros: Works with offline documents Cons: Complex formatting issues

5. OCR (Optical Character Recognition)

Converting images to text (Tesseract, cloud APIs).

Pros: Extracts from images/scans Cons: Accuracy varies, requires preprocessing

Choosing the Right Method

Decision Tree:

1. Is there an API? → Use API (fastest, most reliable)
2. Is content static HTML? → Use web scraping (Cheerio, Beautiful Soup)
3. Is content JavaScript-rendered? → Use browser automation (Puppeteer, Selenium)
4. Is it a document (PDF, Excel)? → Use document parsers
5. Is it an image/scan? → Use OCR

Why Data Extraction Matters

• Business Intelligence: Competitor pricing, market research
• Data Science: Training datasets, analysis
• Automation: Auto-filling forms, monitoring changes
• Integration: Connecting systems without APIs
• Archival: Backing up web content