This dataset was prepared as a beginner's guide to web scraping and data collection. The data is collected from Books to Scrape, a website designed for beginners to learn web scraping. A companion demonstrating how the data was scraped is given here

eagle0504/larkin-web-scrape-dataset-qa-formatted-small-version dataset hosted on Hugging Face and contributed by the HF Datasets community

The commercial centre is anticipated to arrive at USD 886.03 Million in 2025 and is required to develop to USD 4369.4 Million by 2035, recording a CAGR of 17.3% over the figure time frame.

Metric	Value
Market Size (2025E)	USD 886.03 Million
Market Value (2035F)	USD 4369.4 Million
CAGR (2025 to 2035)	17.3%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	24.5%

Country	CAGR (2025 to 2035)
UK	23.8%

Country	CAGR (2025 to 2035)
European Union (EU)	24.0%

Country	CAGR (2025 to 2035)
Japan	24.3%

Country	CAGR (2025 to 2035)
South Korea	24.6%

Competitive Outlook

Company Name	Estimated Market Share (%)
Bright Data (formerly Luminati)	15-20%
ScrapeHero	12-16%
Apify	10-14%
Oxylabs	8-12%
DataDome	6-10%
Other Companies (combined)	35-45%

Discover the booming web scraping tools market! This in-depth analysis reveals a $2831.7 million market in 2025, growing at a CAGR of 14.4% to 2033. Explore key trends, segments (cloud-based, on-premises, retail, finance), top companies, and regional insights. Learn how to leverage web scraping for data-driven decisions.

Description: This dataset contains book information scraped from a fictional online bookstore, intended for educational purposes. The data includes book titles, ratings, and prices and is designed to demonstrate web scraping techniques.

Dataset Features

• Book titles
• Book ratings (1-5 stars)
• Book prices

Dataset Size: 1000 rows

Data Source: Books to Scrape website https://books.toscrape.com/catalogue/page-1.html

Use Cases:

• Web scraping practice and education
• Data cleaning and preprocessing exercises
• Data analysis and visualization projects

The global web scraping software market size was worth over USD 782.5 million in 2025 and is poised to grow at a CAGR of around 13.2%, reaching USD 2.7 billion revenue by 2035, driven by the growing demand for real-time data collection.

arizonapradana/kuh-perdata-pidana-scrape-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

I created this dataset from the information available in books to scrape as part of a initial study in web scrapping.

It's not a very usefull dataset, but it can be good to practice some basic data cleaning, manipulation or visualization.

Columns: 1. Title: the title of the book. 2. Price: the price of the book (since it's fake data, the currency doesn't matter). 3. Rating: the rating of the book. It's range is 1 to 5. 4. Availability: indicates if the book is available in stock or not. 5. Category: the book genre.

Dataset

This dataset was created by Shirsh Mall

Contents

This dataset contains metadata for books collected from the “Books to Scrape” website (http://books.toscrape.com). It includes information about the book title, price, rating, availability, product page URL, and description. The data was scraped for educational and practice purposes. Each row represents one book, and the CSV contains 200+ books from multiple categories.

Pullo-Africa-Protagonist/SCRAPE dataset hosted on Hugging Face and contributed by the HF Datasets community

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Scrape Content Dataset v1

A human-curated benchmark dataset for evaluating web scraping engines on content quality.

  Overview

This dataset contains 1,000 web pages with human-annotated ground truth for evaluating how well web scraping engines capture core content while avoiding noise (navigation, ads, footers, etc.). The dataset was created in 2025-10-21 and may become outdated over time.

  Dataset Structure

CSV format with columns:

id: Sequential identifier url:… See the full description on the dataset page: https://huggingface.co/datasets/firecrawl/scrape-content-dataset-v1.

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

8 Active Global Rubber Scrape suppliers, manufacturers list and Global Rubber Scrape exporters directory compiled from actual Global export shipments of Rubber Scrape.

The global data scraping tools market is booming, projected to hit $2.8 billion in 2025, with a CAGR of 29.1%. Discover key trends, leading companies (Scraper API, Octoparse, etc.), and regional insights in this comprehensive market analysis. Learn how e-commerce, investment, and marketing benefit from data scraping.

The data scraping tools market is experiencing robust growth, driven by the increasing need for businesses to extract valuable insights from vast amounts of online data. The market, estimated at $2 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $6 billion by 2033. This growth is fueled by several key factors, including the exponential rise of big data, the demand for improved business intelligence, and the need for enhanced market research and competitive analysis. Businesses across various sectors, including e-commerce, finance, and marketing, are leveraging data scraping tools to automate data collection, improve decision-making, and gain a competitive edge. The increasing availability of user-friendly tools and the growing adoption of cloud-based solutions further contribute to market expansion. However, the market also faces certain challenges. Data privacy concerns and the legal complexities surrounding web scraping remain significant restraints. The evolving nature of websites and the implementation of anti-scraping measures by websites also pose hurdles for data extraction. Furthermore, the need for skilled professionals to effectively utilize and manage these tools presents another challenge. Despite these restraints, the market's overall outlook remains positive, driven by continuous innovation in scraping technologies, and the growing understanding of the strategic value of data-driven decision-making. Key segments within the market include cloud-based solutions, on-premise solutions, and specialized scraping tools for specific data types. Leading players such as Scraper API, Octoparse, ParseHub, Scrapy, Diffbot, Cheerio, BeautifulSoup, Puppeteer, and Mozenda are shaping market competition through ongoing product development and expansion into new regions.

The global enterprise-grade web scraping service market is estimated to be valued at XXX million in 2023 and is projected to grow at a CAGR of XX% over the forecast period from 2023 to 2033. The market is driven by the increasing demand for data for business intelligence, market research, and customer relationship management. The rising adoption of cloud-based web scraping services, coupled with the growing need for real-time data, is further contributing to the market growth. North America is expected to hold the largest market share during the forecast period due to the presence of a large number of technology companies and the high demand for data-driven insights. Europe is expected to follow North America in terms of market share, driven by the increasing adoption of web scraping services in various industries. The Asia Pacific region is anticipated to witness significant growth in the coming years, owing to the increasing adoption of web scraping services in developing countries. Some of the key players operating in the enterprise-grade web scraping service market include Apify, PromptCloud, DataHen, Agenty, Web Screen Scraping, ScrapeHero, 3i Data Scraping, ReviewGators, Actowiz Solutions, Sequentum, X-Byte, Zyte, Upsilon, IWeb Scraping, BinaryFolks, iWeb Data Scraping, DataForres, Web Scrape, GrowTal, Mozenda, BotScraper, and Octoparse. Website:

Global trade data of Scrape under 70010000, 70010000 global trade data, trade data of Scrape from 80+ Countries.