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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.

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.

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.