Responsible, resilient data extraction: crawlers, headless browsers, parsing, proxies, and the legal ground rules.
Everything you need to know about web scraping — how it works, HTTP vs headless browsers, parsing, crawling at scale, data quality, ethics and legality, and when to use an API instead.
A practical overview of web scraping legality — public vs gated data, terms of service, computer misuse laws, copyright, database rights, GDPR and personal data, and risk-reduction practices.
Learn web scraping with Python step by step — fetching pages with requests and httpx, parsing with BeautifulSoup, pagination, JSON endpoints, async scraping, retries and saving data.
How to scrape dynamic, JavaScript-rendered websites with Playwright — waiting strategies, infinite scroll, intercepting API responses, blocking resources, concurrency and reliability.
How to build production-grade crawlers with Scrapy — spiders, items, pipelines, AutoThrottle and politeness, middlewares, deduplication, feeds, testing, monitoring and deployment.
How to scrape responsibly — reading robots.txt, polite rate limiting, caching, identifying your bot, handling 429s, why sites deploy bot protection, and what to do when you're blocked.
Compare web scraping and APIs for data collection — reliability, cost, legality, data quality, maintenance and speed — with a decision framework and hybrid approaches.
How to design a scalable, reliable scraping system — URL frontier, schedulers, workers, per-domain politeness, raw storage, parsing, validation, change detection and monitoring.
How to use large language models for web scraping — schema-based extraction, cleaning HTML for LLMs, cost control, validation, hybrid selector + LLM pipelines, and when not to use AI.
A worked design for a competitor price monitoring system — product matching, scheduled crawls, price history, change detection, alerts, dashboards, data quality and compliance.