Data Scraping vs Web Scraping: What Is the Difference

Data scraping vs web scraping comes down to the source. Data scraping is the wide term: taking structured records out of anything made for people to read - web pages, but also PDF reports, scanned documents, spreadsheets and the screens of a legacy application. Web scraping is the part that works on websites: product pages, listing pages, search results, seller profiles. Every web scraping project is data scraping; many data scraping projects never open a browser.

For a buyer the label decides who answers the request and what they quote. Ask for "data scraping" and you may get a proposal for reading invoices with OCR when you wanted daily prices from three competitor shops. Ask for "web scraping" for an archive of annual reports in PDF, and you may be quoted crawlers, schedules and proxy rotation you do not need.

The two jobs also fail in different ways. A filed document never changes, but every new layout is new work. A website changes without notice, can show different data to different visitors and may push back against automated traffic, so the cost lasts as long as the collection runs. Below: the axes that matter, a briefing checklist and two of our projects.

Web scraping vs data scraping: which sources each covers

A quick test: if a person had to do the job by hand, would you give them a stack of files or a list of web addresses?

Data scraping: documents, files and old screens

• PDF reports and filings. Annual reports, supplier price lists, tender documents, product data sheets, with the values in paragraphs and tables laid out for print.
• Scanned documents. Invoices, delivery notes, old contracts: OCR comes first, and stamps, signatures and handwriting get in the way.
• Spreadsheets from other people. Partner files with merged cells, totals in the middle of the sheet and a new column order every quarter.
• Screens of older software. A desktop or terminal system that staff read by eye, copied screen by screen.

Web scraping: what a site shows any visitor

• Product pages and search results on marketplaces and retailers: title, price, stock, seller, rating, rank. On our Amazon scraper page the unit is one ASIN per marketplace per run, because several sellers can share an ASIN and the result depends on the delivery address.
• Listing pages on property portals: address, price, size, agent, date listed. On Zillow, as our Zillow scraper page explains, the unit of record is the address: one zpid stays with a home whether it is for sale, for rent, sold or off market.
• Profiles and directories: seller pages, agent pages, business listings.
• Files linked from pages: photos, floor plans, data sheets, filings. Collecting them is web scraping; reading the figures inside a PDF is document work.

Screen scraping vs web scraping

Screen scraping reads what a program draws on screen - an old desktop system, a terminal session, a chart - one screen at a time. Web scraping reads the page a site sends, so it can follow links through a whole category and return the same columns every run. People who say "web screen scraping" usually mean web scraping: name the source, not the technique.

Mobile app scraping vs web scraping

Apps sit closest to web scraping. Some catalogs, prices and stock views exist only inside an app, or differ from the company's website. The job is the same - turn app data into rows on a schedule - and we scope and run it as web scraping; see mobile app scraping.

The difference between data scraping and web scraping in daily work

Documents and screens: every layout is its own job

Document work is front-loaded. Someone maps where each field sits in each layout: the price list with the unit in a footnote, the annual report whose balance sheet runs over two pages. A mapped layout stays mapped, because a filed report does not change. What goes wrong:

• a table split across two pages is read as two tables;
• OCR confuses similar characters and breaks amounts, dates and product codes;
• a new layout arrives unannounced and its values land in the wrong columns.

Websites: the source moves and pushes back

Web work is ongoing. The fields on a product page are easy to find on day one; the effort goes into getting every page, and getting it again next week. What goes wrong:

• Pagination limits. On Amazon a category listing stops at page 400, so a whole category cannot be read by paging alone.
• Data that depends on the visitor. Price, stock and currency follow the delivery address or the country the site assumes.
• Redesigns. A changed layout can empty a column without stopping the run.
• Anti-bot protection. Pacing, proxy rotation, CAPTCHA handling and rendering are part of the job on many commercial sites.
• Deadlines. A daily file is due before the working day, whatever changed overnight.

What the output looks like on each side

From documents: rows tied to the file

A document job returns rows tied to their source: the file name, the page a value came from, your fields and a flag where a value could not be read. SEC EDGAR, as our SEC EDGAR scraper page describes, shows both sides on one site: filings are found by company, form type such as 10-K and filing date, and come in several formats, HTML and PDF among them. Listing the filings is web scraping; taking revenue and debt out of a filing's text is document work.

From websites: one row per item per run

A web job returns one row per product, listing or profile for each run, keyed on the site's own identifier so that runs line up: ASIN and marketplace on Amazon, zpid on Zillow. Typical columns are the identifier, URL, title, price and currency, stock, seller, category path and collection date. What does not fit one row comes as a separate table tied back by the key: our Zillow page describes price history and tax history as child files and photo sets linked by zpid.

Who usually needs which

Document and screen work tends to come from:

• finance and audit teams who need figures from annual reports in a spreadsheet;
• back offices turning invoices and supplier price lists into rows;
• companies moving records out of an old system, screen by screen.

Web scraping tends to come from:

• pricing teams watching competitor prices and stock;
• brands checking how their products and images appear on marketplaces;
• property investors tracking listings, prices and time on market;
• sales teams building lead lists from directories and seller profiles.

If your answer to "where does the data live?" is a list of site addresses or app names, you are buying web scraping. If it is a folder of files or an internal system, you are buying document or screen work.

What drives cost and effort

Document and screen work costs more with:

• more distinct layouts, since each one is mapped on its own;
• poor scans whose numbers need OCR clean-up;
• stricter checking - a figure going into an audit gets read twice.

Web scraping costs more with:

• more sources, plus the work of making their output agree;
• wider coverage: a whole category or country against a short watchlist;
• frequency and deadline: a one-off extract against a daily file due before work starts;
• harder sites: protection against automated traffic, data that changes by location;
• delivery: a CSV for a spreadsheet against a scheduled load into your database.

A large share of web work is upkeep, because sites change. See what web scraping costs and build or buy a scraper.

Where the terms overlap, and how to brief a vendor

The words blur in three places. Many people say "data scraping" when a website is their only source. Websites carry documents - floor plans, data sheets, filings - and a brief has to say whether it wants the link, the file or the values inside it. And crawling gets mixed in: finding pages is one step, taking fields off them another, as our post on crawling and scraping explains; our explainer on web scraping and its uses covers the basics.

A brief that gets the right quote names these points:

1. Sources. Site addresses or app names - or the file types and roughly how many files.
2. Page types. Product pages, search results, listing pages or profiles, and whether you need the page behind each result.
3. Coverage. A whole category, region or catalog, or a list of items you supply.
4. Fields. A field list with one example row filled in by hand.
5. Viewpoint. The country, delivery address, currency and language to read the data in.
6. Files. Links only, or downloaded images and PDFs, and where large files go.
7. Frequency. One-off or a refresh schedule, and when the data must be in place.
8. Output. Format, delivery channel and whether runs must line up for history.
9. Personal data. Whether contact details shown in listings are needed; by default they are left out.

How ScrapeIt runs web scraping projects

ScrapeIt is a managed web scraping service for websites and mobile apps. We build, run and repair the crawlers, and anti-bot handling - pacing, proxy rotation, CAPTCHA handling, rendering - is part of the service. A project starts with a free project assessment, and you can order a sample to hold the columns against your needs. Every dataset comes with field documentation, and runs are one-off or on the schedule you set. Files come as CSV, Excel (XLSX), JSON, JSONLines or XML, through our API, a JSON feed, FTP, SFTP, Amazon S3, Google Cloud Storage, Dropbox, Google Drive, e-mail or a load into your database; an MCP server is available on request. Plans start from EUR 169 a month, or EUR 199 for a one-time extract. See managed web scraping services for how the model works and custom web scraper development for sources that need something built from scratch.

Worked example: whole Amazon categories past the page limit

A lighting manufacturer wanted every product in three Amazon US categories - Smart Home Lighting, Light Bulbs, and Lighting and Ceiling Fans - with the full product page behind each listing, for a complete view of the categories and for image rights checks. Its previous vendor had not got past the page limit: Amazon lets a shopper page through a category listing only up to page 400. We handled that on our side, with the listings read for a US delivery address in Los Angeles.

The hard field was the description, which sellers build themselves. In an early file only 38% of products (4,761) had description text, so the client set the rule: description text in one column, every image link from the description and the brand and manufacturer blocks in another. In under eight weeks of work the client had about 140,000 products in Lighting and Ceiling Fans and 12,300 in Light Bulbs, as CSV and XLSX on FTP. See the Amazon lighting case study.

Worked example: housing.com project pages with every image

A real estate company in India wanted the new-projects section of housing.com: name, builder, address, RERA registration number, configurations, launch date and amenities for each project, plus every image, with floor plans and master plans mattering most. The brief was one run over every project page numbered from 100 to 315,000, and not every number is a project: in the sample run, 1,300 numbers gave 1,000 project cards.

Ten projects in the first test sample came with 160-170 images, so the images went straight into the client's cloud drive, with a mapping sheet tying each file to its project. Each brochure was delivered as one whole PDF rather than 30 to 50 page images - the choice in point six of the checklist, settled during scoping. The last images were in the client's drive three weeks after the 1,000-row sample. Read the housing.com case study or our Housing.com scraper page.

FAQ

Is data scraping the same as web scraping?

Not quite. Web scraping is the kind of data scraping that works on websites; data scraping also covers PDFs, scans, spreadsheets and the screens of older software. In a brief, name the source rather than the term.

What is screen scraping, and when do I need it?

Screen scraping reads the values a program shows on screen, one screen at a time - typically an older desktop or terminal system. You need it when the records exist only in such a program. If they are on a website, ask for web scraping instead.

Is web scraping legal?

We collect only publicly available data - everything a visitor can see on the site - and we collect it legally. Names, phone numbers and e-mail addresses of private persons are personal data: we handle them under GDPR and leave such fields out by default. Contact details a listing shows openly can be delivered when a project needs them.

Which one does my project need?

Look at where the data lives. Site addresses or app names mean web scraping; a folder of files or an internal system means document or screen work. If a site keeps PDFs behind its links, say whether you need the files themselves or the values inside them.