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

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

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

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	24.5%

Country	CAGR (2025 to 2035)
UK	23.8%

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

Country	CAGR (2025 to 2035)
Japan	24.3%

Country	CAGR (2025 to 2035)
South Korea	24.6%

Competitive Outlook

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

Dataset

This dataset was created by Shirsh Mall

Contents

The Web Scraping Market is Segmented by Solution (Software, Services), Deployment Type (Cloud, On-Premise), End-User Industry (BFSI, Retail and E-Commerce, Real Estate, Manufacturing, Government, Healthcare, Advertising and Media, and More), Use Case (Data Scaping / ETL, Price and Competitive Monitoring, and More), and Geography.

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

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅232M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

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

Dataset

This dataset was created by Me. _ .et1808

Released under Apache 2.0

Contents

Web scraping market was valued at USD 754.17 million in 2024 and is projected to reach USD 2,870.33 million by 2034...

The Web Scraping Software Market is estimated to be valued at USD 501.9 million in 2025 and is projected to reach USD 2030.4 million by 2035, registering a compound annual growth rate (CAGR) of 15.0% over the forecast period.

Metric	Value
Web Scraping Software Market Estimated Value in (2025 E)	USD 501.9 million
Web Scraping Software Market Forecast Value in (2035 F)	USD 2030.4 million
Forecast CAGR (2025 to 2035)	15.0%

Discover the booming web scraping tools market! This in-depth analysis reveals a 15% CAGR, key players like Octoparse and Scrapy, and future trends shaping this $3226.7 million market (2025). Learn about market drivers, restraints, and regional breakdowns to gain a competitive edge.

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

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How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The global web scraper software market size is projected to grow from USD 814.40 million in 2025 to USD 2209.88 million by 2033, exhibiting a CAGR of 13.29%.
Report Scope:

Report Metric	Details
Market Size in 2024	USD 718.86 Million
Market Size in 2025	USD 814.40 Million
Market Size in 2033	USD 2209.88 Million
CAGR	13.29% (2025-2033)
Base Year for Estimation	2024
Historical Data	2021-2023
Forecast Period	2025-2033
Report Coverage	Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends
Segments Covered	By Type,By Application,By Vertical,By Region.
Geographies Covered	North America, Europe, APAC, Middle East and Africa, LATAM,
Countries Covered	U.S., Canada, U.K., Germany, France, Spain, Italy, Russia, Nordic, Benelux, China, Korea, Japan, India, Australia, Taiwan, South East Asia, UAE, Turkey, Saudi Arabia, South Africa, Egypt, Nigeria, Brazil, Mexico, Argentina, Chile, Colombia,

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.

The size of the Web Scraping Software and Platform market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

Introduction

The global web scraping market reached USD 754.17 million in 2024 and is projected to grow to USD 2,870.33 million by 2034, registering a strong CAGR of 14.3%. Growth is driven by rising data-driven decision-making, expansion of e-commerce analytics, competitive intelligence needs, and increased automation across enterprises. North America led the market with a 42.4% share in 2024, valued at USD 319.76 million. The US alone contributed USD 286.51 million in 2024 and is anticipated to reach USD 930.39 million by 2034 at a CAGR of 12.5%.

https://market.us/wp-content/uploads/2025/10/Web-Scraping-Market-Size.png" alt="">

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.

italoxesteres/cvm-web-scraping dataset hosted on Hugging Face and contributed by the HF Datasets community

Global Web Scraping Services market size 2025 was XX Million. Web Scraping Services Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The dataset obtained from web scraping encompasses a diverse set of news articles from prominent sources: Al Jazeera, BBC News Arabic, Fatabyyano, Verify-Sy and matsda2sh. Each article provides unique insights into various topics, ranging from global politics and current affairs to health, culture, and technology. The dataset offers a comprehensive snapshot of contemporary news coverage, allowing for in-depth analysis and exploration of different perspectives. With detailed information on article titles, categories, publication dates, and content, researchers and analysts can gain valuable insights into arabic media trends, public discourse, and societal issues.