What Is Scraping and How Do You Extract Web Data?

Scraping is the automated extraction of specific data from web pages — prices, reviews, contact details, posts — and it differs from crawling in one key way: a crawler discovers and follows links across a site, while a scraper targets defined fields on pages you already know about. You can start scraping today by picking a ready-to-run scraper for your target site (for example, a TikTok or Google Maps scraper on Apify), running it with a URL or search query, and exporting the results. Building your own scraper makes sense only when no existing tool covers your site or data shape.

Scraping vs. crawling: the distinction that matters

Both use the same underlying mechanics — fetch a page, parse the HTML — but they answer different questions.

In practice they chain together: a crawler finds the 500 product pages, then a scraper pulls the price and title from each. Apify's Website Content Crawler, for instance, crawls sites and extracts text content specifically to feed AI models, LLM applications, vector databases, or RAG pipelines — that is crawling and extraction in one step.

Common scraping use cases

The data people actually extract tends to fall into a few buckets:

• Price and catalog monitoring — track price details over time or compare offerings across e-commerce sites. Apify's E-commerce Scraping Tool is built for exactly this, scraping almost any retail site in minutes.
• Lead generation — pull business names, addresses, phone numbers, emails, and job titles. The Google Maps Scraper extracts reviews, reviewer details, images, contact info, opening hours, and prices from thousands of locations.
• Social media data — posts, engagement metrics, hashtags, comments, and follower counts. The TikTok Scraper handles videos, hashtags, and users; the Instagram Scraper covers posts, reels, profiles, places, carousels, and comments; the Facebook Posts Scraper pulls captions, reactions, video transcripts, and external links.
• AI and RAG pipelines — clean text content to ground a model or populate a vector store.

Ready-to-run scraper or build your own?

This is the first real decision, and it usually comes down to whether your target is already covered.

Choose a ready-to-run scraper when:

• Your target is a major platform (TikTok, Instagram, Google Maps, Facebook, most retail sites) — these already have maintained tools with thousands of users and public ratings.
• You want results today, not a development project.
• You need scheduling, API access, and export without writing that plumbing yourself.

Build your own when:

• Your target is niche, internal, or has no existing tool.
• You need a custom data shape or fields no general scraper exposes.
• You're willing to maintain it as the site changes.

Apify's marketplace lists tens of thousands of tools, and the popular ones carry usage counts and ratings you can use as a signal — the Google Maps Scraper shows 616K runs and a 4.7 rating across 1,817 reviews; the Instagram Scraper shows 402K runs and 4.7 across 597. High run counts with stable ratings generally mean the tool is actively maintained against site changes.

How to run a scraper and get your data out

The workflow is consistent across most ready-to-run tools:

1. Open the scraper and find its input form.
2. Provide input — typically one or more URLs, or a search query. For example, the TikTok Scraper accepts URLs or search queries to scrape profiles, hashtags, posts, shares, followers, hearts, names, and music-related data.
3. Run it. The tool fetches and parses the pages.
4. Export the results — usually JSON, CSV, or Excel.
5. Automate if needed — run the scraper via API, schedule recurring runs, and monitor them. Most Apify tools support all three, and integrate with other tools or AI workflows.

The expected result is a structured dataset you can load into a spreadsheet, database, or downstream app — not a saved webpage.

Troubleshooting: blocked requests and missing data

Two problems account for most failed runs.

Blocked or empty responses. Sites detect and block automated traffic. Symptoms: zero results, CAPTCHA pages, or HTTP 403s. Mitigations, in order of effort:

• Use a maintained scraper rather than a homegrown one — the maintainer handles anti-bot changes for you.
• Slow down and reduce concurrency.
• Use proxies or rotating IPs if the tool supports them.

Missing or malformed fields. Usually one of three causes:

• The site changed its HTML structure — the scraper's selectors are now stale. Check for a tool update or report it.
• The field isn't present on every page (some listings have no price, some profiles have no email). Your parser should tolerate nulls.
• The data loads via JavaScript after the initial HTML — a scraper that only reads static HTML will miss it. Use a tool that renders the page.

A practical check before blaming the tool: run it on a single known-good URL first. If that works and a batch fails, the issue is rate limiting or blocking, not extraction logic.

Where to start

Pick the scraper matching your target platform, run it once on a single URL to confirm it returns the fields you need, then scale up with scheduling and API access. If no existing tool fits, that's your signal to build a custom Actor — but check the marketplace first, since the popular tools already handle the export, API, scheduling, and monitoring work you'd otherwise rebuild.