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 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.

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/mendoanjoe/crawl-data.

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.

Host-level Web Graph - This graph aggregates the page graph by subdomain/host where each node represents a specific subdomain/host and an edge exists between a pair of hosts/subdomains if at least one link was found between pages that belong to a pair of subdomains/hosts. 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-firstlevel-subdomain and web-cc12-PayLevelDomain.

Context

Common Crawl project has fascinated me ever since I learned about it. It provides a large number of data formats and presents challenges across skill and interest areas. I am particularly interested in URL analysis for applications such as typosquatting, malicious URLs, and just about anything interesting that can be done with domain names.

Content

I have sampled 1% of the domains from the Common Crawl Index dataset that is available on AWS in Parquet format. You can read more about how I extracted this dataset @ https://harshsinghal.dev/create-a-url-dataset-for-nlp/

Acknowledgements

Thanks a ton to the folks at https://commoncrawl.org/ for making this immensely valuable resource available to the world for free. Please find their Terms of Use here.

Inspiration

My interests are in working with string similarity functions and I continue to find scalable ways of doing this. I wrote about using a Postgres extension to compute string distances and used Common Crawl URL domains as the input dataset (you can read more @ https://harshsinghal.dev/postgres-text-similarity-with-commoncrawl-domains/).

I am also interested in identifying fraudulent domains and understanding malicious URL patterns.

The Turkish web corpus MaCoCu-tr 1.0 was built by crawling the ".tr" internet top-level domain in 2021, extending the crawl dynamically to other domains as well (https://github.com/macocu/MaCoCu-crawler).

Considerable efforts were devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.

Each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score (based on a language model). The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality and fluency, the automatically identified language of the text in the paragraph, and information whether the paragraph contains personal information.

This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.

Market Overview and Growth Drivers: The global live crawling service market is projected to witness significant growth over the forecast period from 2025 to 2033. In 2025, the market is estimated to be valued at XXX million, and it is expected to expand at a CAGR of XX% during the forecast period. The primary drivers behind this growth include the increasing demand for data analytics, the growing adoption of data scraping tools, and the emergence of advanced crawling technologies. Moreover, the rising trend of e-commerce and the need for real-time data for business intelligence and competitive analysis are contributing to the market expansion. Market Segmentation and Regional Analysis: The live crawling service market is segmented based on application, type, and region. By application, the market is classified into SMEs and large enterprises. By type, the market is divided into web data crawling, PDF data crawling, and others. Geographically, the market is divided into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is expected to hold a dominant share in the market due to the presence of key players such as X-Byte Enterprise Crawling and Actowiz Solutions, as well as the high adoption of data analytics and data scraping tools in the region. Asia Pacific is anticipated to witness the fastest growth rate during the forecast period, attributed to the rapid growth of the e-commerce sector and the increasing demand for data for market research and competitive analysis.

CCNet Reproduced Split (4M rows, 3.7B Tokens (Mistral tokenizer))

  Overview

This dataset is a reproduced subset of the larger CCNet dataset, tailored specifically to facilitate easier access and processing for researchers needing high-quality, web-crawled text data for natural language processing tasks. The CCNet dataset leverages data from the Common Crawl, a non-profit organization that crawls the web and freely provides its archives to the public. This subset contains 4… See the full description on the dataset page: https://huggingface.co/datasets/JorgeeGF/CCNet.

The live crawling service market is experiencing robust growth, driven by the increasing need for real-time data insights across various sectors. Businesses are increasingly relying on up-to-the-minute information to optimize their SEO strategies, monitor brand reputation, and gain a competitive edge. The market's expansion is fueled by the rising adoption of advanced analytics, the proliferation of e-commerce, and the growing demand for personalized user experiences. Key players like X-Byte Enterprise Crawling, Actowiz Solutions, PromptCloud, and DataForSEO are actively shaping the market landscape through continuous innovation and expansion of their service offerings. The increasing complexity of website architectures and the need for efficient data extraction are also contributing to market growth. While data security and privacy concerns present potential restraints, the ongoing development of robust security protocols and compliance measures is mitigating these challenges. We estimate the market size to be approximately $500 million in 2025, with a Compound Annual Growth Rate (CAGR) of 15% projected from 2025 to 2033. This translates to a significant market expansion over the forecast period. Segmentation within the live crawling service market includes different pricing models, service levels, and target industries. Geographic variations also exist, with North America and Europe currently dominating the market share due to higher adoption rates and technological advancements. However, Asia-Pacific is anticipated to show significant growth in the coming years driven by expanding digital economies and increasing internet penetration. The competitive landscape is marked by both established players and emerging startups, leading to innovation in service offerings and pricing strategies. This dynamic market is expected to continue its strong growth trajectory, driven by technological innovation and the increasing reliance on real-time data across a broad range of industries.

The Common Crawl Creative Commons Corpus (C5)

Raw CommonCrawl crawls, annotated with Creative Commons license information

C5 is an effort to collect Creative Commons-licensed web data in one place. The licensing information is extracted from the web pages based on whether they link to Creative Commons licenses either overtly in a tags (like in the footer of Wikipedia) or in metadata fields indicating deliberate Creative Commons publication. However, false positives may occur! See… See the full description on the dataset page: https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons.

This repository contains quality scores for the OWS datasets listed in Table 1 in [1]. The scores are computed with the QT5-small model trained by Chang et al [2] as outlined in [1] (containerised approach). For storage efficiency, we provide only the quality scores, not the full metadata files. However, the folder structure is the same as in the original dataset (as identified with the unique ID provided by the OWLER dashboard) for compatibility. The scores are arranged in the same order as the documents in the metadata parquet-files, where a file 'scores_0.txt' contains the scores for the documents in 'metadata_0.parquet' in the same folder in the original dataset. It is to be noted that the quality scores denote the log-probability of the document being relevant to any query.

[1] Pezzuti, F., Mueller, A., MacAvaney, S. & Tonellotto, N. (2025, April). Document Quality Scoring for Web Crawling. In The Second International Workshop on Open Web Search (WOWS).

[2] Chang, X., Mishra, D., Macdonald, C., & MacAvaney, S. (2024, July). Neural Passage Quality Estimation for Static Pruning. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 174-185).

In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this paper, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license.

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.

Microformat, Microdata and RDFa data from the October 2016 Common Crawl web corpus. We found structured data within 1.24 billion HTML pages out of the 3.2 billion pages contained in the crawl (38%). These pages originate from 5.63 million different pay-level-domains out of the 34 million pay-level-domains covered by the crawl (16.5%). Altogether, the extracted data sets consist of 44.2 billion RDF quads.

Dataset

This dataset was created by Farol Nguyen

Contents

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.

This project develop components of a polar cyberinfrastructure (CI) to support researchers and users for data discovery and access. The main goal is to provide tools that will enable a better access to polar data and information, hence allowing to spend more time on analysis and research, and significantly less time on discovery and searching. A large-scale web crawler, PolarHub, is developed to continuously mine the Internet to discover dispersed polar data. Beside identifying polar data in major data repositories, PolarHub is also able to bring individual hidden resources forward, hence increasing the discoverability of polar data. Quality and assessment of data resources are analyzed inside of PolarHub, providing a key tool for not only identifying issues but also to connect the research community with optimal data resources.

In the current PolarHub system, seven different types of geospatial data and processing services that are compliant with OGC (Open Geospatial Consortium) are supported in the system. They are: -- OGC Web Map Service (WMS): is a standard protocol for serving (over the Internet)georeferenced map images which a map server generates using data from a GIS database. -- OGC Web Feature Service (WFS): provides an interface allowing requests for geographical features across the web using platform-independent calls. -- OGC Web Coverage Service (WCS): Interface Standard defines Web-based retrieval of coverages; that is, digital geospatial information representing space/time-varying phenomena. -- OGC Web Map Tile Service (WMTS): is a standard protocol for serving pre-rendered georeferenced map tiles over the Internet. -- OGC Sensor Observation Service (SOS): is a web service to query real-time sensor data and sensor data time series and is part of theSensor Web. The offered sensor data comprises descriptions of sensors themselves, which are encoded in the Sensor Model Language (SensorML), and the measured values in the Observations and Measurements (O and M) encoding format. -- OGC Web Processing Service (WPS): Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for invoking geospatial processing services, such as polygon overlay, as a web service. -- OGC Catalog Service for the Web (CSW): is a standard for exposing a catalogue of geospatial records in XML on the Internet (over HTTP). The catalogue is made up of records that describe geospatial data (e.g. KML), geospatial services (e.g. WMS), and related resources.

PolarHub has three main functions: (1) visualization and metadata viewing of geospatial data services; (2) user-guided real-time data crawling; and (3) data filtering and search from PolarHub data repository.

Dataset Card for CC-News

  Dataset Summary

CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has been prepared using news-please - an integrated web crawler and information extractor for news.It contains 708241 English language news articles published between Jan 2017 and December 2019. It represents a small portion of the English… See the full description on the dataset page: https://huggingface.co/datasets/vblagoje/cc_news.

Dataset

This dataset was created by Firuz Juraev

Contents