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Scraping: guides & tutorials

Turn messy web pages into clean datasets: fetch, parse, scale, and respect site limits—practical patterns for engineers shipping scrapers with Apify.

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Scraping turns messy web pages into clean, structured datasets you can analyze or automate against. These guides cover the whole craft: fetching pages efficiently, parsing HTML, scaling crawls with queues and concurrency, and respecting site limits so your jobs stay reliable and well-behaved.

The right approach depends on the target. Static pages need only HTTP requests and a parser, while dynamic sites call for headless browsers and proxies. Apify and Crawlee handle retries, rotation, and storage so you focus on extraction logic. Below you will find practical tutorials for engineers shipping scrapers from first script to production.

Related topics

Apify7 min read

Using Claude to Generate Python Scrapers for Apify (2026)

· 7 min read
Yassine El Haddad
Software Developer & Automation Specialist

Describe your scraping target to Claude and get Python Apify Actor code. Deploy with apify push. Claude does not run the scraper for you: it generates code you review, test, and deploy. New to Claude? You can try Claude free for a week to draft and iterate on Actor code before you commit. For pre-built targets, check the Apify Store first. Claude shines when you need a custom target: an internal portal, a niche marketplace, or a site with a unique layout. The Store covers popular sites (Indeed, Amazon, LinkedIn); Claude fills the gaps. Expect 15–30 minutes from prompt to first successful run for a simple target.

Architecture4 min read

Instagram Intelligence: Bypassing GraphQL Hashes for Media Data (2026)

· 4 min read
Yassine El Haddad
Software Developer & Automation Specialist

Instagram is a heavily protected ecosystem. Teams still need public metrics like follower counts, reel velocity, and comment signals for influencer vetting, but automated collection is actively restricted.

Running an Instagram extraction workflow in 2026 means understanding SPA behavior, GraphQL/mobile endpoints, and IP reputation controls.

This guide outlines common failure modes in basic scripts and shows a more durable path using the Apify Platform.

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Frequently asked questions

Frequently Asked Questions

Web scraping automates data extraction from websites by fetching HTML or JSON responses, parsing them with selectors or path expressions, and storing the results. The process involves crawling (discovering URLs), scraping (extracting fields), and storing (writing to datasets or databases). Tools range from simple scripts to full platforms like Apify.

Define your data requirements, identify target URLs, inspect page structure in DevTools to locate data fields, choose an appropriate tool (Cheerio for static HTML, Playwright for JavaScript), write extraction code, validate outputs against your schema, and schedule on Apify for automated fresh data. Start small, test thoroughly, then scale.

JavaScript rendering requires browser automation. Anti-bot systems block datacenter IPs and detect automation signatures. CSS selectors break when sites redesign. Rate limits require polite crawl pacing. Legal constraints vary by jurisdiction and use case. Each challenge has established solutions; the key is recognizing which applies to your specific target site.

Finance: alternative data for trading signals. E-commerce: competitive pricing intelligence. Real estate: listing aggregation. HR: job market trend analysis. Marketing: brand monitoring and SEO research. AI/ML: training data collection. Lead generation: contact enrichment. Nearly every data-driven industry uses some form of web scraping.