How I Approach Reliable Web Scraping with Python

In real projects, web scraping is less about extracting data once and more about making the process reliable and maintainable.

My usual approach is:

1. Check for an API first
   
   If the site exposes JSON data through network requests, I prefer direct HTTP requests over browser automation.

2. Use Scrapy for structured crawling
   
   For larger scraping jobs, Scrapy is usually my first choice because it handles requests, pipelines, retries, and concurrency well.

3. Use Playwright only when needed
   
   If the site depends heavily on JavaScript, authentication, or browser state, I use Playwright or Selenium.

4. Handle pagination and missing data carefully
   
   Scrapers should expect multiple pages, null values, changed fields, and temporary failures.

5. Validate the output
   
   I check record counts, duplicates, missing values, and schema changes so problems are detected early.

My preferred order is usually:

Direct HTTP/API → Scrapy → Browser automation

The goal is to use the simplest approach that remains reliable in production.