Discover the booming market for data scraping tools! This comprehensive analysis reveals a $2789.5 million market in 2025, growing at a 27.8% CAGR. Explore key trends, regional insights, and leading companies shaping this dynamic sector. Learn how to leverage data scraping for your business.

Explore the expanding global Data Extraction Software Tools market (valued at $1185M, CAGR 2.3%), driven by AI, cloud adoption, and increasing data volumes for SMEs and large organizations. Discover key trends, restraints, and regional insights for 2025-2033.

The booming data extraction service market is projected to reach $47.4 Billion by 2033, growing at a 15% CAGR. Discover key market trends, leading companies, and regional insights in this comprehensive analysis of web scraping, API extraction, and more. Learn how to leverage data for better decision-making.

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 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.

The analysis of occupants’ perception can improve building indoor environmental quality (IEQ). Going beyond conventional surveys, this study presents an innovative analysis of occupants’ feedback about the IEQ of different workplaces based on web-scraping and text-mining of online job reviews. A total of 1,158,706 job reviews posted on Glassdoor about 257 large organizations (with more than 10,000 employees) are scraped and analyzed. Within these reviews, 10,593 include complaints about at least one IEQ aspect. The analysis of this large number of feedbacks referring to several workplaces is the first of its kind and leads to two main results: (1) IEQ complaints mostly arise in workplaces that are not office buildings, especially regarding poor thermal and indoor air quality conditions in warehouses, stores, kitchens, and trucks; (2) reviews containing IEQ complaints are more negative than reviews without IEQ complaints. The first result highlights the need for IEQ investigations beyond office buildings. The second result strengthens the potential detrimental effect that uncomfortable IEQ conditions can have on job satisfaction. This study demonstrates the potential of User-Generated Content and text-mining techniques to analyze the IEQ of workplaces as an alternative to conventional surveys, for scientific and practical purposes.

Background and methodsSystematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain steps in systematic reviews, such as data extraction, are labour-intensive, which hampers their feasibility, especially with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed to develop a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n = 45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n = 31 publications; multiple sclerosis, n = 244 publications).ResultsOur data mining tool, STEED (STructured Extraction of Experimental Data), successfully extracted key experimental parameters such as animal models and species, as well as risk of bias items like randomization or blinding, from in vivo studies. Sensitivity and specificity were over 85% and 80%, respectively, for most items in both validation corpora. Accuracy and F1-score were above 90% and 0.9 for most items in the validation corpora, respectively. Time savings were above 99%.ConclusionsOur text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool’s deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews.

The LSC (Leicester Scientific Corpus)August 2019 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk) Supervised by Prof Alexander Gorban and Dr Evgeny MirkesThe data is extracted from the Web of Science® [1] You may not copy or distribute this data in whole or in part without the written consent of Clarivate Analytics.Getting StartedThis text provides background information on the LSC (Leicester Scientific Corpus) and pre-processing steps on abstracts, and describes the structure of files to organise the corpus. This corpus is created to be used in future work on the quantification of the sense of research texts. One of the goal of publishing the data is to make it available for further analysis and use in Natural Language Processing projects.LSC is a collection of abstracts of articles and proceeding papers published in 2014, and indexed by the Web of Science (WoS) database [1]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English.The corpus was collected in July 2018 online and contains the number of citations from publication date to July 2018.Each document in the corpus contains the following parts:1. Authors: The list of authors of the paper2. Title: The title of the paper3. Abstract: The abstract of the paper4. Categories: One or more category from the list of categories [2]. Full list of categories is presented in file ‘List_of _Categories.txt’.5. Research Areas: One or more research area from the list of research areas [3]. Full list of research areas is presented in file ‘List_of_Research_Areas.txt’.6. Total Times cited: The number of times the paper was cited by other items from all databases within Web of Science platform [4]7. Times cited in Core Collection: The total number of times the paper was cited by other papers within the WoS Core Collection [4]We describe a document as the collection of information (about a paper) listed above. The total number of documents in LSC is 1,673,824.All documents in LSC have nonempty abstract, title, categories, research areas and times cited in WoS databases. There are 119 documents with empty authors list, we did not exclude these documents.Data ProcessingThis section describes all steps in order for the LSC to be collected, clean and available to researchers. Processing the data consists of six main steps:Step 1: Downloading of the Data OnlineThis is the step of collecting the dataset online. This is done manually by exporting documents as Tab-delimitated files. All downloaded documents are available online.Step 2: Importing the Dataset to RThis is the process of converting the collection to RData format for processing the data. The LSC was collected as TXT files. All documents are extracted to R.Step 3: Cleaning the Data from Documents with Empty Abstract or without CategoryNot all papers have abstract and categories in the collection. As our research is based on the analysis of abstracts and categories, preliminary detecting and removing inaccurate documents were performed. All documents with empty abstracts and documents without categories are removed.Step 4: Identification and Correction of Concatenate Words in AbstractsTraditionally, abstracts are written in a format of executive summary with one paragraph of continuous writing, which is known as ‘unstructured abstract’. However, especially medicine-related publications use ‘structured abstracts’. Such type of abstracts are divided into sections with distinct headings such as introduction, aim, objective, method, result, conclusion etc.Used tool for extracting abstracts leads concatenate words of section headings with the first word of the section. As a result, some of structured abstracts in the LSC require additional process of correction to split such concatenate words. For instance, we observe words such as ConclusionHigher and ConclusionsRT etc. in the corpus. The detection and identification of concatenate words cannot be totally automated. Human intervention is needed in the identification of possible headings of sections. We note that we only consider concatenate words in headings of sections as it is not possible to detect all concatenate words without deep knowledge of research areas. Identification of such words is done by sampling of medicine-related publications. The section headings in such abstracts are listed in the List 1.List 1 Headings of sections identified in structured abstractsBackground Method(s) DesignTheoretical Measurement(s) LocationAim(s) Methodology ProcessAbstract Population ApproachObjective(s) Purpose(s) Subject(s)Introduction Implication(s) Patient(s)Procedure(s) Hypothesis Measure(s)Setting(s) Limitation(s) DiscussionConclusion(s) Result(s) Finding(s)Material (s) Rationale(s)Implications for health and nursing policyAll words including headings in the List 1 are detected in entire corpus, and then words are split into two words. For instance, the word ‘ConclusionHigher’ is split into ‘Conclusion’ and ‘Higher’.Step 5: Extracting (Sub-setting) the Data Based on Lengths of AbstractsAfter correction of concatenate words is completed, the lengths of abstracts are calculated. ‘Length’ indicates the totalnumber of words in the text, calculated by the same rule as for Microsoft Word ‘word count’ [5].According to APA style manual [6], an abstract should contain between 150 to 250 words. However, word limits vary from journal to journal. For instance, Journal of Vascular Surgery recommends that ‘Clinical and basic research studies must include a structured abstract of 400 words or less’[7].In LSC, the length of abstracts varies from 1 to 3805. We decided to limit length of abstracts from 30 to 500 words in order to study documents with abstracts of typical length ranges and to avoid the effect of the length to the analysis. Documents containing less than 30 and more than 500 words in abstracts are removed.Step 6: Saving the Dataset into CSV FormatCorrected and extracted documents are saved into 36 CSV files. The structure of files are described in the following section.The Structure of Fields in CSV FilesIn CSV files, the information is organised with one record on each line and parts of abstract, title, list of authors, list of categories, list of research areas, and times cited is recorded in separated fields.To access the LSC for research purposes, please email to ns433@le.ac.uk.References[1]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[2]WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html[3]Research Areas in WoS. Available: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html[4]Times Cited in WoS Core Collection. (15 July). Available: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Times-Cited-accessibility-and-variation?language=en_US[5]Word Count. Available: https://support.office.com/en-us/article/show-word-count-3c9e6a11-a04d-43b4-977c-563a0e0d5da3[6]A. P. Association, Publication manual. American Psychological Association Washington, DC, 1983.[7]P. Gloviczki and P. F. Lawrence, "Information for authors," Journal of Vascular Surgery, vol. 65, no. 1, pp. A16-A22, 2017.

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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.

Abstract This aim of this paper is the acquisition of geographic data from the Foursquare application, using data mining to perform exploratory and spatial analyses of the distribution of tourist attraction and their density distribution in Rio de Janeiro city. Thus, in accordance with the Extraction, Transformation, and Load methodology, three research algorithms were developed using a tree hierarchical structure to collect information for the categories of Museums, Monuments and Landmarks, Historic Sites, Scenic Lookouts, and Trails, in the foursquare database. Quantitative analysis was performed of check-ins per neighborhood of Rio de Janeiro city, and kernel density (hot spot) maps were generated The results presented in this paper show the need for the data filtering process - less than 50% of the mined data were used, and a large part of the density of the Museums, Historic Sites, and Monuments and Landmarks categories is in the center of the city; while the Scenic Lookouts and Trails categories predominate in the south zone. This kind of analysis was shown to be a tool to support the city's tourist management in relation to the spatial localization of these categories, the tourists’ evaluations of the places, and the frequency of the target public.

Coral reefs are popular for their vibrant biodiversity. By combining Web-scraped Instagram data from tourists and high-resolution live coral cover maps in Hawaii, we find that, regionally, coral reefs both attract and suffer from coastal tourism. Higher live coral cover attracts reef visitors, but that visitation contributes to subsequent reef degradation. Such feedback loops threaten the highest-quality reefs, highlighting both their economic value and the need for effective conservation management.

This repository contains the raw Instagram post data used to run these analyses as well as the Python script used to generate this dataset. The base Python script was adapted from code written by Zoe Volenec.

Dataset Description

This dataset contains PDF-to-text conversions of scientific research articles, prepared for the task of data citation mining. The goal is to identify references to research datasets within full-text scientific papers and classify them as Primary (data generated in the study) or Secondary (data reused from external sources).

The PDF articles were processed using MinerU, which converts scientific PDFs into structured machine-readable formats (JSON, Markdown, images). This ensures participants can access both the raw text and layout information needed for fine-grained information extraction.

Files and Structure

Each paper directory contains the following files:

• *_origin.pdf The original PDF file of the scientific article.

• *_content_list.json Structured extraction of the PDF content, where each object represents a text or figure element with metadata. Example entry:

  {
   "type": "text",
   "text": "10.1002/2017JC013030",
   "text_level": 1,
   "page_idx": 0
  }
  

• full.md The complete article content in Markdown format (linearized for easier reading).

• images/ Folder containing figures and extracted images from the article.

• layout.json Page layout metadata, including positions of text blocks and images.

Data Mining Task

The aim is to detect dataset references in the article text and classify them:

• DOIs (Digital Object Identifiers): https://doi.org/[prefix]/[suffix] Example: https://doi.org/10.5061/dryad.r6nq870

• Accession IDs: Used by data repositories. Format varies by repository. Examples:

  • GSE12345 (NCBI GEO)
  • PDB 1Y2T (Protein Data Bank)
  • E-MEXP-568 (ArrayExpress)

Each dataset mention must be labeled as:

• Primary: Data generated by the paper (new experiments, field observations, sequencing runs, etc.).
• Secondary: Data reused from external repositories or prior studies.

Training and Test Splits

• train/ → Articles with gold-standard labels (train_labels.csv).
• test/ → Articles without labels, used for evaluation.

• train_labels.csv → Ground truth with:

  • article_id: Research paper DOI.
  • dataset_id: Extracted dataset identifier.
  • type: Citation type (Primary / Secondary).

• sample_submission.csv → Example submission format.

Example

Paper: https://doi.org/10.1098/rspb.2016.1151 Data: https://doi.org/10.5061/dryad.6m3n9 In-text span:

"The data we used in this publication can be accessed from Dryad at doi:10.5061/dryad.6m3n9." Citation type: Primary

This dataset enables participants to develop and test NLP systems for:

• Information extraction (locating dataset mentions).
• Identifier normalization (mapping mentions to persistent IDs).
• Citation classification (distinguishing Primary vs Secondary data usage).

Proxy Server Service Market size was valued at USD 3.5 Billion in 2024 and is projected to reach USD 8.2 Billion by 2032, growing at a CAGR of 10.3% during the forecast period 2026-2032.Rising concerns over online data exposure are addressed by deploying proxy servers to anonymize user activity and protect sensitive information. Usage is supported across corporate networks and individual users to ensure browsing confidentiality.

A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure–activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 2470771  Domain Name : careerbuilder.usa.com  Date Range : 01st Jul 2021 - 30th Sep 2021   File Extension : ldjson

Available Fields : url, job_title, category, company_name, logo_url, city, state, country, post_date, test_months_of_experience, test_educational_credential, occupation_category, job_description, job_type, valid_through, html_job_description, extra_fields, test_onetsoc_code, test_onetsoc_name, uniq_id, crawl_timestamp, apply_url, job_board, geo, job_post_lang, inferred_iso2_lang_code, is_remote, test1_cities, test1_states, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, has_expired, last_expiry_check_date, latest_expiry_check_date, dataset, postdate_in_indexname_format, segment_name, duplicate_status, job_desc_char_count, fitness_score    

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

The Supplementary Material of the paper "Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining" includes: 1) APPENDIX 1: SQL Statements for data extraction. Appendix 2: Interview for operating Staff. 2) The DataSet of the normalized data to define the predictive model.

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 1093713  Domain Name : monter.usa.com  Date Range : 01st April 2022 - 31st June 2022   File Extension : ldjson

Available Fields : url, job_title, category, company_name, city, state, country, post_date, occupation_category, job_description, job_type, valid_through, html_job_description, extra_fields, uniq_id, crawl_timestamp, job_board, geo, job_post_lang, inferred_iso2_lang_code, is_remote, test1_cities, test1_states, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, has_expired, last_expiry_check_date, latest_expiry_check_date, dataset, postdate_in_indexname_format, segment_name, duplicate_status, job_desc_char_count, ijp_reprocessed_flag_1, ijp_reprocessed_flag_2, ijp_reprocessed_flag_3, ijp_is_production_ready, fitness_score  

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.