The global web crawler tool market is experiencing robust growth, driven by the increasing need for data extraction and analysis across diverse sectors. The market's expansion is fueled by the exponential growth of online data, the rise of big data analytics, and the increasing adoption of automation in business processes. Businesses leverage web crawlers for market research, competitive intelligence, price monitoring, and lead generation, leading to heightened demand. While cloud-based solutions dominate due to scalability and cost-effectiveness, on-premises deployments remain relevant for organizations prioritizing data security and control. The large enterprise segment currently leads in adoption, but SMEs are increasingly recognizing the value proposition of web crawling tools for improving business decisions and operations. Competition is intense, with established players like UiPath and Scrapy alongside a growing number of specialized solutions. Factors such as data privacy regulations and the complexity of managing web crawlers pose challenges to market growth, but ongoing innovation in areas such as AI-powered crawling and enhanced data processing capabilities are expected to mitigate these restraints. We estimate the market size in 2025 to be $1.5 billion, growing at a CAGR of 15% over the forecast period (2025-2033). The geographical distribution of the market reflects the global nature of internet usage, with North America and Europe currently holding the largest market share. However, the Asia-Pacific region is anticipated to witness significant growth driven by increasing internet penetration and digital transformation initiatives across countries like China and India. The ongoing development of more sophisticated and user-friendly web crawling tools, coupled with decreasing implementation costs, is projected to further stimulate market expansion. Future growth will depend heavily on the ability of vendors to adapt to evolving web technologies, address increasing data privacy concerns, and provide robust solutions that cater to the specific needs of various industry verticals. Further research and development into AI-driven crawling techniques will be pivotal in optimizing efficiency and accuracy, which in turn will encourage wider adoption.

A corpus of web crawl data composed of 5 billion web pages. This data set is freely available on Amazon S3 at s3://aws-publicdatasets/common-crawl/crawl-002/ and formatted in the ARC (.arc) file format.

Common Crawl is a non-profit organization that builds and maintains an open repository of web crawl data for the purpose of driving innovation in research, education and technology. This data set contains web crawl data from 5 billion web pages and is released under the Common Crawl Terms of Use.

The Common Crawl corpus contains petabytes of data collected over 8 years of web crawling. The corpus contains raw web page data, metadata extracts and text extracts. Common Crawl data is stored on Amazon Web Services’ Public Data Sets and on multiple academic cloud platforms across the world.

Discover the booming Web Crawler Tool market! This analysis reveals key trends, drivers, and restraints, plus a detailed look at leading companies like Scrapy, Mozenda, and UiPath. Learn about market size projections, CAGR, and regional market share for informed decision-making.

Armenian language dataset extracted from CC-100 research dataset Description from website This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.

DATAANT provides the ability to extract data from any website using its web scraping service.

Receive raw HTML data by triggering the API or request a custom dataset from any website.

Use the received data for: - data analysis - data enrichment - data intelligence - data comparison

The only two parameters needed to start a data extraction project: - data source (website URL) - attributes set for extraction

All the data can be delivered using the following: - One-Time delivery - Scheduled updates delivery - DB access - API

All the projects are highly customizable, so our team of data specialists could provide any data enrichment.

Discover the ultimate web scraping tool of 2024 for unlocking valuable insights. Effortlessly extract web data for ecommerce, real estate, and beyond. Harness the power of web scraping to drive informed decision-making and gain a competitive edge.

According to Cognitive Market Research, The Global Anti crawling Techniques market size is USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of 6.00% from 2023 to 2030.

North America Anti crawling Techniques held the major market of more than 40% of the global revenue and will grow at a compound annual growth rate (CAGR) of 4.2% from 2023 to 2030.
Europe Anti crawling Techniques accounted for a share of over 30% of the global market and are projected to expand at a compound annual growth rate (CAGR) of 4.5% from 2023 to 2030.
Asia Pacific Anti crawling Techniques held the market of more than 23% of the global revenue and will grow at a compound annual growth rate (CAGR) of 8.0% from 2023 to 2030.
South American Anti crawling Techniques market of more than 5% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.4% from 2023 to 2030.
Middle East and Africa Anti crawling Techniques held the major market of more than 2% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.7% from 2023 to 2030.
The market for anti-crawling techniques has grown dramatically as a result of the increasing number of data breaches and public awareness of the need to protect sensitive data. 
Demand for bot fingerprint databases remains higher in the anti crawling techniques market.
The content protection category held the highest anti crawling techniques market revenue share in 2023.

Increasing Demand for Protection and Security of Online Data to Provide Viable Market Output

The market for anti-crawling techniques is expanding due in large part to the growing requirement for online data security and protection. Due to an increase in digital activity, organizations are processing and storing enormous volumes of sensitive data online. Organizations are being forced to invest in strong anti-crawling techniques due to the growing threat of data breaches, illegal access, and web scraping occurrences. By protecting online data from harmful activity and guaranteeing its confidentiality and integrity, these technologies advance the industry. Moreover, the significance of protecting digital assets is increased by the widespread use of the Internet for e-commerce, financial transactions, and sensitive data transfers. Anti-crawling techniques are essential for reducing the hazards connected to online scraping, which is a tactic often used by hackers to obtain important data.

Increasing Incidence of Cyber Threats to Propel Market Growth

The growing prevalence of cyber risks, such as site scraping and data harvesting, is driving growth in the market for anti-crawling techniques. Organizations that rely significantly on digital platforms run a higher risk of having illicit data extracted. In order to safeguard sensitive data and preserve the integrity of digital assets, organizations have been forced to invest in sophisticated anti-crawling techniques that strengthen online defenses. Moreover, the market's growth is a reflection of growing awareness of cybersecurity issues and the need to put effective defenses in place against changing cyber threats. Moreover, cybersecurity is constantly challenged by the spread of advanced and automated crawling programs. The ever-changing threat landscape forces enterprises to implement anti-crawling techniques, which use a variety of tools like rate limitation, IP blocking, and CAPTCHAs to prevent fraudulent scraping efforts.

Market Restraints of the Anti crawling Techniques

Increasing Demand for Ethical Web Scraping to Restrict Market Growth

The growing desire for ethical web scraping presents a unique challenge to the anti-crawling techniques market. Ethical web scraping is the process of obtaining data from websites for lawful objectives, such as market research or data analysis, but without breaching the terms of service. Furthermore, the restraint arises because anti-crawling techniques must distinguish between criminal and ethical scraping operations, finding a balance between preventing websites from misuse and permitting authorized data harvest. This dynamic calls for more complex and adaptable anti-crawling techniques to distinguish between destructive and ethical scrapping actions.

Impact of COVID-19 on the Anti Crawling Techniques Market

The demand for online material has increased as a result of the COVID-19 pandemic, which has...

The global data scraping tools market, valued at $15.57 billion in 2025, is experiencing robust growth. While the provided CAGR is missing, a reasonable estimate, considering the expanding need for data-driven decision-making across various sectors and the increasing sophistication of web scraping techniques, would be between 15-20% annually. This strong growth is driven by the proliferation of e-commerce platforms generating vast amounts of data, the rising adoption of data analytics and business intelligence tools, and the increasing demand for market research and competitive analysis. Businesses leverage these tools to extract valuable insights from websites, enabling efficient price monitoring, lead generation, market trend analysis, and customer sentiment monitoring. The market segmentation shows a significant preference for "Pay to Use" tools reflecting the need for reliable, scalable, and often legally compliant solutions. The application segments highlight the high demand across diverse industries, notably e-commerce, investment analysis, and marketing analysis, driving the overall market expansion. Challenges include ongoing legal complexities related to web scraping, the constant evolution of website structures requiring adaptation of scraping tools, and the need for robust data cleaning and processing capabilities post-scraping. Looking forward, the market is expected to witness continued growth fueled by advancements in artificial intelligence and machine learning, enabling more intelligent and efficient scraping. The integration of data scraping tools with existing business intelligence platforms and the development of user-friendly, no-code/low-code scraping solutions will further boost adoption. The increasing adoption of cloud-based scraping services will also contribute to market growth, offering scalability and accessibility. However, the market will also need to address ongoing concerns about ethical scraping practices, data privacy regulations, and the potential for misuse of scraped data. The anticipated growth trajectory, based on the estimated CAGR, points to a significant expansion in market size over the forecast period (2025-2033), making it an attractive sector for both established players and new entrants.

This dataset is compiled from Common Crawl data.

A sample of Common Crawl WARC files was processed and URLs to their Javascript libraries were mapped.

Common Crawl https://commoncrawl.org/ publishes an Internet-wide crawl every year (sometimes multiple times a year). These files capture very rich information of the websites that were crawled.

I wanted to build something like BuiltWith which tells you what technologies a website is using. One place to look for this is what Javascript libraries are being used by the website.

I'm actually writing a book on how to build a data product for those who own a keyboard.

You can read along as I continue writing the book at https://buildadataproduct.netlify.app/

An automatic pipeline based on an algorithm that identifies new resources in publications every month to assist the efficiency of NIF curators. The pipeline is also able to find the last time the resource's webpage was updated and whether the URL is still valid. This can assist the curator in knowing which resources need attention. Additionally, the pipeline identifies publications that reference existing NIF Registry resources as this is also of interest. These mentions are available through the Data Federation version of the NIF Registry, http://neuinfo.org/nif/nifgwt.html?query=nlx_144509 The RDF is based on an algorithm on how related it is to neuroscience. (hits of neuroscience related terms). Each potential resource gets assigned a score (based on how related it is to neuroscience) and the resources are then ranked and a list is generated.

Login Pages HTTP Client Hints Dataset

HTTP client hint crawling data of all login pages of the 8M Tranco list websites.

This data set contains the crawled Accept-CH HTTP header values on all Tranco-list-related login pages from August 2022 to December 2023. You can use the data set to reproduce our study results regarding the client hint usage on the Web.

We crawled the data from three different continents (North America: Johnstown, Ohio, USA; Europe: Frankfurt and Biere, Germany; Asia: Singapore) and two different Internet Service Providers (ISP), which were Amazon Web Services (AWS) and Deutsche Telekom (DT).

Overview

You can find the crawling data inside the crawl_data_redacted folder of this repository. It is subdivided into our four different crawling regions, which are also the subfolders:

• eu_otc: Crawling data from Biere, Germany (Europe), using the DT ISP.
• eu_aws: Crawling data from Frankfurt, Germany (Europe), using the AWS ISP.
• ap_aws: Crawling data from Singapore (Asia), using the AWS ISP.
• us_aws: Crawling data from Johnstown, Ohio, USA (North America), using the AWS ISP.

Each folder includes the following files:

• crawl_data_login_urls_only.csv: Contains the responses from all crawled login URLs
• crawl_data_clustered_third_party_urls_only.csv: Contains the responses from requests to third party URLs that were initiated by the login URLs
• crawl_data_trackerlist_urls_only.csv: Contains the responses from requests to third-party URLs that were identified as trackers and initiated by the login URLs.

General

Each data set file contains the following columns:

Data Creation

We used the Tranco List from June 21st, 2022 and visited all 8M hostnames of this list with a crawler bot to identify their login pages. We then crawled the login pages on a monthly basis and recorded the Accept-CH HTTP header sent by each website. For technical reasons, we had crawling gaps of one (October 2022) and two months (October/November 2023). However, the impact should be minimal (see Publication).

Publication

You can find more details on our conducted study in the following journal article:

A Privacy Measure Turned Upside Down? Investigating the Use of HTTP Client Hints on the Web
Stephan Wiefling, Marian Hönscheid, and Luigi Lo Iacono.
19th International Conference on Availability, Reliability and Security (ARES '24), Vienna, Austria

Bibtex

...

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

This dataset is the result of three crawls of the web performed in May 2018. The data contains raw crawl data and instrumentation captured by OpenWPM-Mobile, as well as analysis that identifies which scripts access mobile sensors, which ones perform some of browser fingerprinting, as well as clustering of scripts based on their intended use. The dataset is described in the included README.md file; more details about the methodology can be found in our ACM CCS'18 paper: Anupam Das, Gunes Acar, Nikita Borisov, Amogh Pradeep. The Web's Sixth Sense: A Study of Scripts Accessing Smartphone Sensors. In Proceedings of the 25th ACM Conference on Computer and Communications Security (CCS), Toronto, Canada, October 15–19, 2018. (Forthcoming)

More and more websites have started to embed structured data describing products, people, organizations, places, events into their HTML pages using markup standards such as RDFa, Microdata and Microformats. The Web Data Commons project extracts this data from several billion web pages. The project provides the extracted data for download and publishes statistics about the deployment of the different formats.

The web scraper software market offers a range of solutions tailored to different needs and complexities: General-Purpose Web Crawlers: These versatile tools are designed to extract data from a wide variety of websites with diverse structures and content. Popular examples include UiPath and Octoparse, offering robust features and flexibility for broad-scale scraping projects. Specialized Web Crawlers: These solutions are optimized for specific websites or domains, providing enhanced efficiency and accuracy for targeted data extraction. Scrapinghub is a notable example, offering specialized tools and integrations for specific web applications. Incremental Web Crawlers: Designed for ongoing data updates, these crawlers focus on identifying and extracting only newly added or modified content, ensuring datasets remain current and relevant. Mozenda exemplifies this category, providing tools for efficient monitoring and updating. Deep Web Crawlers: These advanced tools access data residing within hidden or protected sections of the web that are not readily accessible through traditional methods. DeepCrawl is an example of a platform designed to navigate and extract data from these typically less accessible areas of the internet. Recent developments include: March 2022: KaraMD announced Pure Health Apple Cider Vinegar Gummies, a vegan gummy aimed to aid ketosis, digestion regulation, weight management, and encourage greater levels of energy., January 2022: Solace Nutrition, a US-based medical nutrition company, bought R-Kane Nutritionals' assets for an unknown sum. This asset acquisition enables Solace Nutrition to develop synergy between both brands, accelerate growth, and establish a position in an adjacent nutrition sector. R-Kane Nutritionals is a firm established in the United States that specializes in high-protein meal replacement products for weight loss., February 2021: Hydroxycut's newest creation, CUT Energy, a delectable clean energy drink, was released. This powerful mix was carefully formulated for regular energy drink consumers, exercise enthusiasts, and dieters looking to lose weight..

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AI Driven Web Scraping Market Size 2025-2029

The AI driven web scraping market size is valued to increase USD 3.16 billion, at a CAGR of 39.4% from 2024 to 2029. Surging demand for data-driven insights and business intelligence will drive the ai driven web scraping market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Type - Dynamic scraping segment was valued at USD 82.90 billion in 2023
By Application - E-commerce and retail segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 3159.00 million
CAGR from 2024 to 2029 : 39.4%

Market Summary

The AI-driven web scraping market is experiencing significant growth, fueled by the increasing demand for data-driven insights and business intelligence. The rise of Large Language Model (LLM) and the democratization of web scraping through no-code and low-code platforms are key drivers, enabling businesses to extract valuable data from the web more efficiently and effectively than ever before. These advancements enable businesses to extract valuable data from the web more efficiently and effectively than ever before. However, this growth comes with challenges. The sophistication of anti-scraping technologies is escalating, requiring advanced techniques and technologies to bypass these barriers.
According to recent estimates, the global web scraping market is projected to reach USD12.5 billion by 2027, underscoring its growing importance in the digital business landscape. Despite these challenges, the future of AI-driven web scraping is bright, offering businesses a powerful tool to gain a competitive edge in today's data-driven economy.

What will be the Size of the AI Driven Web Scraping Market during the forecast period?

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How is the AI Driven Web Scraping Market Segmented ?

The ai driven web scraping industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Dynamic scraping
  Static scraping
  API-based scraping


Application

  E-commerce and retail
  Finance and banking
  Market research
  Cyber security
  Others


Deployment

  Cloud-based
  On-premises
  Hybrid


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The dynamic scraping segment is estimated to witness significant growth during the forecast period.

The AI-driven web scraping market continues to evolve, with the services segment, or Data as a Service (DaaS,) gaining significant traction. In this model, clients outsource the entire data acquisition process to specialized companies, specifying their data requirements, including target websites and desired data fields, while the service provider manages the technical aspects. This approach is ideal for organizations lacking the in-house expertise, infrastructure, or time for complex web scraping operations. The integration of artificial intelligence is crucial for scalability and efficiency, enabling distributed scraping systems, data validation rules, and data visualization dashboards. Machine learning models power link extraction techniques, image recognition algorithms, and natural language processing, while proxy server management, unstructured data processing, and data cleaning pipelines ensure legal compliance frameworks.

Data transformation rules and structured data parsing facilitate API integration strategies, and headless browser automation, error handling mechanisms, and rate limiting protocols maintain ethical scraping guidelines. The market's growth is evident in the 50% annual increase in companies using cloud storage solutions for data storage and real-time data streaming.

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The Dynamic scraping segment was valued at USD 82.90 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How AI Driven Web Scraping Market Demand is Rising in North America Request Free Sample

The market is experiencing significant growth and evolution, with North America leading the charge. This region, particularly the United States, boasts the largest and most mature market due to its advanced technological infrastructure, the presence of leadi

Page-level Web Graph - Nodes represent a single web page (url in a web site) and each edge represents a hyperlink between two pages. The hyperlink graph was extracted from the Web corpus released by the Common Crawl Foundation in August 2012. The Web corpus was gathered using a web crawler employing a breadth-first-search selection strategy and embedding link discovery while crawling. The crawl was seeded with a large number of URLs from former crawls performed by the Common Crawl Foundation. Also, see web-cc12-hostgraph and web-cc12-PayLevelDomain. Note this is the finest granularity of the web graph among the three from web-cc12.

How to use this data?

After download this repo, use cat to get zip file: cat baidu_wiki_part_* > merge.zip

Then simply, unzip this zip file unzip merge.zip

  What is in this data?





  Image(Screenshot)

Raw images are in images folder. /wikihow$ ls data/images | head -5 1111-4.jpg 111-15.jpg 1-draw-7.png 20200613_130717.jpg 22-19.jpg

  Index page

Index page is a collection of web urls. This is how we start to crawl these websites. wikihow$ cat… See the full description on the dataset page: https://huggingface.co/datasets/Bofeee5675/GUI-Net-Crawler.