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.

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.

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.

This is a semi-cleaned dataset containing information from job posts related to data science field. The data is scraped from 4 websites and the process is done in December 2023. Langchain framework from OpenAI was used to support the data extraction task. For example, getting the soft skills and tools that the job post's description mention.

Here is the data schema for this data set

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14229286%2Fcd5c6bc8700ad49f34a48b61981625c4%2Fimage%20(2).png?generation=1703998231851462&alt=media" alt="">

31/12/2023: The data set's description is not finished.

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...

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.

A sample of the Common Crawl dataset. The archive has 38,079 rows, and is one of 80,000 samples.

"The Common Crawl corpus contains petabytes of data collected since 2008. It contains raw web page data, extracted metadata and text extractions."

https://commoncrawl.org/

WET Response Format: "As many tasks only require textual information, the Common Crawl dataset provides WET files that only contain extracted plaintext. The way in which this textual data is stored in the WET format is quite simple. The WARC metadata contains various details, including the URL and the length of the plaintext data, with the plaintext data following immediately afterwards."

Web Crawling Software Market Outlook

According to our latest research, the global web crawling software market size reached USD 1.85 billion in 2024, driven by the exponential growth in data-driven decision-making across industries. The market is expected to grow at a robust CAGR of 16.2% during the forecast period, reaching an estimated USD 7.68 billion by 2033. This impressive growth is primarily fueled by the increasing demand for automated data extraction, real-time market intelligence, and digital transformation initiatives worldwide. As organizations seek to harness the power of big data for competitive advantage, web crawling software is becoming an essential tool for extracting, aggregating, and analyzing relevant information from the vast expanse of the internet.

One of the most significant growth factors for the web crawling software market is the accelerated adoption of digital technologies across sectors such as e-commerce, BFSI, IT, and healthcare. Enterprises are increasingly leveraging web crawling solutions to automate the collection of large volumes of unstructured data from various online sources, which is then used to drive business intelligence, monitor competition, and optimize strategies. The proliferation of online platforms, coupled with the need for timely and accurate data, has made web crawling software indispensable for organizations aiming to stay agile and responsive in dynamic market environments. Furthermore, the integration of artificial intelligence and machine learning with web crawling tools is enhancing their ability to deliver deeper insights and more sophisticated analytics.

Another key driver is the growing importance of price monitoring and market intelligence in highly competitive industries. Retailers, e-commerce platforms, and financial institutions are utilizing web crawling software to track competitor pricing, product availability, and emerging market trends in real time. This capability not only empowers businesses to adjust their offerings proactively but also enables them to identify new opportunities and mitigate risks associated with market volatility. Additionally, regulatory requirements and compliance mandates are pushing organizations, especially in the BFSI sector, to deploy web crawling solutions for risk assessment, fraud detection, and compliance monitoring, further boosting market demand.

The surge in lead generation and customer acquisition efforts is also contributing to the expansion of the web crawling software market. Companies across various sectors are using automated web crawlers to identify potential leads, analyze customer sentiment, and personalize marketing campaigns. The scalability and efficiency offered by these tools allow organizations to streamline their sales pipelines and enhance conversion rates. Moreover, the increasing prevalence of cloud-based deployment models is making web crawling software more accessible to small and medium enterprises (SMEs), democratizing access to advanced data extraction capabilities and leveling the playing field with larger competitors.

From a regional perspective, North America currently dominates the web crawling software market, accounting for a substantial share due to its mature IT infrastructure, high digital adoption rates, and strong presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitization, expanding e-commerce ecosystems, and the increasing adoption of data analytics in countries such as China, India, and Japan. Europe also holds a significant market share, driven by stringent regulatory requirements and a growing emphasis on data-driven business strategies. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by digital transformation initiatives and rising investments in information technology.

Component Analysis

The web crawling software market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment encompasses the core platforms and tools that automate the extraction, aggregation, and analysis of web data. These solutions are continuously evolving, with vendors incorporating advanced features such as natural language processing, sentiment analysis, and real-time data processing to cater to diverse business needs. The increasing demand for customizable and s

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.

81368 Global import shipment records of Crawler with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Dataset

This dataset was created by Long Vũ Hoàng

Released under MIT

Contents

A subset of Common Crawl, extracted from Colossally Cleaned Common Crawl (C4) dataset with the additional constraint that extracted text safely encodes to ASCII. A Unigram tokenizer of vocabulary 12.228k tokens is provided, along with pre-tokenized data.

Originally designed to carry the towering Saturn V moon rocket from the Vehicle Assembly Building to the seaside launch site, the enormous transporters now carry the space shuttles to the launch pads for liftoff. Polygons: 146050 Vertices: 141658

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.

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.

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. 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).

68026 Global import shipment records of Crawler Excavator with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Yoox Products Database is a comprehensive, ready-to-use dataset featuring over 250,000 product listings from the Yoox online fashion platform. This database is ideal for eCommerce analytics, price comparison tools, trend forecasting, competitor research, and building product recommendation engines.

Inside, you’ll find structured CSV files neatly compressed in a ZIP archive, making it simple to import into any BI tool, database, or application.

Key Data Fields:

• Product IDs & SKUs

• Product Titles & Descriptions

• Categories & Subcategories

• Brand Information

• Pricing & Discounts

• Availability & Stock Status

• Image Links

Perfect for data analysts, developers, marketers, and online retailers looking to harness fashion retail insights.