ScrapeIt Blog

Six Polish ecommerce websites worth scraping in 2026, from Allegro and Ceneo to Castorama and Biedronka: the fields each site shows, what only it adds, how to merge them into one dataset and what our Allegro and Castorama projects needed.

Six German property websites worth scraping in 2026 - ImmoScout24, Immowelt, Kleinanzeigen, Sparkasse Immobilien, ohne-makler.net and WG-Gesucht - with the fields each one shows, what only it adds and how to merge them into one dataset.

Eight Spanish ecommerce websites worth scraping in 2026, from El Corte Ingles and Mercadona to Zara and Wallapop: the fields each one shows, what only it adds, what its data is best for and how the sites merge into one dataset.

Eleven UK ecommerce websites worth scraping in 2026, from Tesco and Sainsbury's to ASOS, Boots and Gumtree: what each site shows, the fields a data buyer needs, what only it adds and how to merge them into one dataset.

Twelve US car websites compared as data sources, from Autotrader and CarGurus to Copart, IAA and the RV sites: what each shows, which fields need care - VIN, trim, deal labels, damage codes - and how to merge them into one dataset.

Ten German ecommerce websites worth scraping in 2026, from OTTO and idealo to REWE and Edeka: what each site shows, which fields matter to a data buyer, what is specific to German retail data and how to combine the sites into one dataset.

Seven UK property websites compared as data sources: Rightmove, Zoopla, OnTheMarket, OpenRent, SpareRoom, Savills and Knight Frank. What each one shows, which fields need care - tenure, EPC, price qualifiers, sold STC - and how to merge them into one dataset.

If you work in travel tech, an OTA, a hotel chain, or at an airport, you are in a price-and-availability arms race. Fares change by the hour, room inventory disappears in minutes, and competitors test new bundles and ancillaries constantly.

If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.

E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.

Real estate teams work in a fragmented data landscape. The sites that drive demand in Boston look nothing like the ones that matter in Berlin, São Paulo, Dubai, or Mumbai.

Real estate teams are operating in a data environment that is bigger, faster, and more fragmented than ever. Listings go live and disappear in hours, price cuts happen quietly, and the portals that matter most in each country are rarely the same global “top 5.”

Six jobs that scraped property data does, each backed by a ScrapeIt project: seller leads, listing monitoring, image datasets, weekly market supply, sold records and unit-level rentals. For every use case - the fields it needs and how often to refresh them.

Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.

Where opinion text lives on the public web, what a review record contains on each kind of site, which fields a model needs besides the text, and what quietly breaks a sentiment dataset - with examples from our own projects.

Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.

Using web scraping to extract Google Maps data will help you quickly and efficiently find businesses in any industry, city, state, or region. And the extracted contact information, such as phone or email address, social networks, or links to web pages, will help to contact them.

Finding winning products to sell is quite a difficult process, especially if you are new to e-commerce. Nevertheless, it is the most important task for any dropshipping business.

Web scraping automates the extraction and aggregation of financial data, makes it easier to find stocks, and allows you to predict the market based on the information.

With millions of products online, finding them for sale can seem like a daunting task. Therefore, there are many analytical and research tools available to help narrow down your search for profitable products in order to speed up the process.

Lead scraping is a reliable way to get the right customer contacts to market your product, saves organizations time, and helps you better understand the audience you want to attract. Contacts include emails, phone numbers, or social media profiles.

Data and analytics are opening the door to uncovering ways to combat financial crime based on smart data. And advanced AI analytics and cognitive techniques, machine learning, and automation will improve the inefficiency of existing investigative processes.

Web scraping in eCommerce is a fundamental tool that helps retailers collect data on what people are searching for online, analyze trends, and uncover opportunities for online promotions. This way, you can focus on using marketing strategies that align with what your customers actually want.

Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.

Thanks to real estate web scraping, you can get exhaustive and up-to-date information about properties. Read this article to get to know the essence of this technology and learn how exactly you can benefit from it.

If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.

Web crawling and web scraping are not synonyms. Read this article to get to know the difference between these two terms.