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Dataset about the prices of the oil and its products with and without taxes across the years1.0

Web Scraping Tools Market Outlook

The global web scraping tools market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.5% during the forecast period. The growing demand for web scraping tools is primarily driven by the increasing need for data extraction and analysis across various industries. These tools have become essential in gathering competitive intelligence, monitoring prices, conducting market research, and generating leads, which are critical activities for businesses looking to maintain a competitive edge in a data-driven world.

One of the primary growth factors for the web scraping tools market is the exponential increase in data generation on the internet. With the proliferation of e-commerce, social media, and other online platforms, businesses need to collect vast amounts of data to analyze consumer behavior, market trends, and competitor strategies. Web scraping tools enable automated data extraction from various online sources, providing businesses with valuable insights that can inform decision-making and strategic planning. Moreover, advancements in machine learning and artificial intelligence have enhanced the capabilities of these tools, making them more efficient and accurate in extracting relevant data.

Another significant growth driver is the rising adoption of web scraping tools by small and medium enterprises (SMEs). These enterprises often lack the resources to conduct extensive market research or data analysis in-house. Web scraping tools offer a cost-effective solution for SMEs to gather critical business intelligence without substantial investment in manual data collection. Furthermore, the availability of cloud-based web scraping solutions has made these tools more accessible to SMEs, enabling them to leverage scalable and flexible data extraction capabilities without the need for significant infrastructure or technical expertise.

The increasing application of web scraping tools across various industry verticals is also contributing to market growth. Industries such as retail and e-commerce, banking, financial services, and insurance (BFSI), healthcare, media and entertainment, and information technology and telecommunications are leveraging these tools for various purposes. For instance, in the retail sector, web scraping tools are used for price monitoring and competitive analysis, while in the BFSI sector, they assist in fraud detection and risk management. The growing demand for these applications is expected to drive the adoption of web scraping tools across different industries.

Data Extraction Software plays a pivotal role in the web scraping ecosystem, providing the backbone for efficient data collection processes. These software solutions are designed to handle vast amounts of data from diverse online sources, ensuring that businesses can access the information they need for strategic decision-making. With the increasing complexity of data available on the internet, Data Extraction Software has evolved to include advanced features such as machine learning algorithms and artificial intelligence capabilities. These enhancements allow for more precise and accurate data extraction, enabling businesses to gain deeper insights into market trends and consumer behavior. As industries continue to rely on data-driven strategies, the demand for robust Data Extraction Software is expected to grow, further fueling the expansion of the web scraping tools market.

From a regional perspective, North America holds the largest market share for web scraping tools, driven by the high adoption of advanced technologies and a strong presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation and increasing internet penetration in countries like China and India. The growing number of start-ups and SMEs in the region is also contributing to the rising demand for web scraping tools. Europe and Latin America are also experiencing steady growth, driven by the increasing focus on data-driven decision-making and business intelligence.

Type Analysis

The web scraping tools market can be segmented by type into browser extensions, standalone software, cloud-based so

Web Scraper Software Market Valuation – 2024-2031

Web Scraper Software Market was valued at USD 568.2 Million in 2024 and is projected to reach USD 1628.6 Million by 2031, growing at a CAGR of 14.1% from 2024 to 2031.

Global Web Scraper Software Market Drivers

Data-Driven Decision Making: Businesses increasingly rely on data-driven insights to make informed decisions. Web scraping tools enable organizations to collect large amounts of structured and unstructured data from various websites, empowering them to analyze market trends, consumer behavior, and competitor activities.

Price Intelligence: E-commerce businesses utilize web scraping to monitor competitor pricing, identify pricing opportunities, and optimize their own pricing strategies.

Market Research and Analysis: Web scraping tools help researchers and analysts gather data on market trends, consumer sentiment, and industry benchmarks. This data is invaluable for conducting in-depth market research and analysis.

Global Web Scraper Software Market Restraints

Ethical and Legal Considerations: Web scraping can raise ethical and legal concerns, particularly when it violates website terms of service or copyright laws. It's crucial to adhere to ethical guidelines and respect website owners' rights.

Technical Challenges: Web scraping can be technically complex, requiring knowledge of programming languages like Python and libraries such as Beautiful Soup and Scrapy. Additionally, websites often implement anti-scraping measures, making data extraction challenging.

Web scraping is a tool for extracting information from the underlying HTML code of websites. ONS has been conducting research into these technologies and, since May 2014, has been scraping prices from the websites of three retailers. Last year, ONS released two updates that constructed experimental price indices from the data. In this release, we provide updates to the experimental indices, and an analysis of the different methods used to clean and classify the data.

Web Scraping Services Market is segmented By Type (Browser Extension, Installable Software, and Cloud Based) and Application (Data Aggregation, Customer Insight, and Others)

The web scraping software and platform market is experiencing robust growth, driven by the increasing demand for real-time data insights across diverse sectors. Businesses are leveraging web scraping to gather competitive intelligence, enhance market research, monitor brand reputation, and power personalized customer experiences. The market's expansion is fueled by the proliferation of publicly available online data and the rising adoption of sophisticated data analytics techniques. While challenges like website changes, legal and ethical considerations surrounding data scraping, and the need for robust data management solutions persist, the overall market outlook remains positive. The availability of both open-source and commercial solutions caters to a broad spectrum of users, from individual developers to large enterprises, further contributing to the market's dynamism. We estimate the market size in 2025 to be $2.5 billion, based on observed growth in related data analytics markets and the increasing adoption of web scraping technologies. A compound annual growth rate (CAGR) of 15% is projected for the forecast period (2025-2033), indicating significant potential for market expansion. This growth is propelled by several key trends, including the rise of big data analytics, increased automation in business processes, and the growing adoption of cloud-based solutions. The market is segmented by software type (cloud-based, on-premise), application (market research, price intelligence, lead generation), and industry vertical (e-commerce, finance, media). Leading players are continuously innovating to offer advanced features like intelligent data extraction, real-time data processing, and seamless integration with other business intelligence tools. However, restraints such as the complexity of implementing web scraping solutions, potential for legal repercussions related to data privacy, and the need for skilled professionals to manage these systems pose challenges to market growth. Despite these hurdles, the long-term prospects remain strong, driven by ongoing technological advancements and the enduring need for accurate, up-to-date data across various industries.

The global data scraping tools market is experiencing robust growth, projected to reach $2.802 billion in 2025, fueled by a compound annual growth rate (CAGR) of 29.1%. This expansion is driven by the increasing need for businesses to leverage large datasets for informed decision-making across various sectors. E-commerce companies rely heavily on web scraping for price comparison, competitor analysis, and market research. Similarly, investment analysts utilize these tools for gathering financial data and conducting market sentiment analysis. Marketing professionals employ data scraping for lead generation, social media monitoring, and customer behavior analysis. The market is segmented into "Pay-to-Use" and "Free-to-Use" models, with "Pay-to-Use" solutions expected to dominate due to their advanced features and scalability. Geographic growth is diverse, with North America currently holding a significant market share, followed by Europe and Asia Pacific. However, the Asia Pacific region is anticipated to witness faster growth due to increasing digitalization and e-commerce penetration. The market faces certain restraints, such as concerns over data privacy regulations and the increasing sophistication of website anti-scraping measures. However, continuous innovation in data scraping technologies, addressing these challenges, is likely to sustain the market's strong growth trajectory. The increasing adoption of cloud-based solutions and the rise of AI-powered scraping tools further contribute to the market's expansion. The competitive landscape includes both established players and emerging startups. Key players such as Scraper API, Octoparse, ParseHub, Scrapy, Diffbot, Cheerio, BeautifulSoup, Puppeteer, and Mozenda are constantly innovating to enhance the capabilities of their tools. The future of the data scraping tools market is promising, driven by the growing reliance on data-driven decision-making across industries and the continuing advancements in technology. This growth presents lucrative opportunities for both established and emerging players, encouraging further investments and innovation in this dynamic sector. The market’s future growth is dependent on factors such as technological advancements, regulatory compliance, and the evolving needs of various industry verticals.

Context: This dataset contains flight fare data that was collected from the EaseMyTrip website using web scraping techniques. The data was collected with the goal of providing users with information that could help them make informed decisions about when and where to purchase flight tickets. By analyzing patterns in flight fares over time, users can identify the best times to book tickets and potentially save money.

Sources: 1. Data collected using Python script with Beautiful Soup and Selenium libraries. 2. Script collected data on various flight details such as Date of booking, Date of travel, Airline and class, Departure time and source, Arrival time and destination, Duration, Total stops, Price. 3. The scraping process was designed to collect data for flights departing from a specific set of airports (Top 7 busiest airports in India). Note that the Departure Time feature also includes the Source airport, and the Arrival Time feature also includes the Destination airport. Which is later extracted in Cleaned_dataset. Also both cleaned and scraped datasets have provided so that one can use dataset as per their requirement and convenience.

Inspiration: 1. Dataset created to provide users with valuable resource for analyzing flight fares in India. 2. Detailed information on flight fares over time can be used to develop more accurate pricing models and inform users about best times to book tickets. 3. Data can also be used to study trends and patterns in the travel industry through air can act as a valuable resource for researchers and analysts.

Limitations: 1. This dataset only covers flights departing from specific airports and limited to a certain time period. 2. To perform time series analysis one have gather data for at least top 10 busiest airports for 365 days. 3. This does not cover variations in aviation fuel prices as this is the one of influencing factor for deciding fare, hence the same dataset might not be useful for next year, but I will try to update it twice in an year. 4. Also demand and supply for the particular flight seat is not available in the dataset as this data is not publicly available on any flight booking web site.

Scope of Improvement: 1. The dataset could be enhanced by including additional features such as current aviation fuel prices and the distance between the source and destination in terms of longitude and latitude. 2. The data could also be expanded to include more airlines and more airports, providing a more comprehensive view of the flight market. 3. Additionally, it may be helpful to include data on flight cancellations, delays, and other factors that can impact the price and availability of flights. 4. Finally, while the current dataset provides information on flight prices, it does not include information on the quality of the flight experience, such as legroom, in-flight amenities, and customer reviews. Including this type of data could provide a more complete picture of the flight market and help travelers make more informed decisions.

The global web screen scraping tools market is experiencing robust growth, driven by the increasing need for businesses to extract valuable data from websites for various applications. The market size in 2025 is estimated at $3,877.7 million. While the exact CAGR isn't provided, considering the rapid advancements in data analytics and the expanding reliance on online data, a conservative estimate of the CAGR between 2025 and 2033 would be around 15%. This growth is fueled by the rising adoption of web scraping across diverse sectors, including e-commerce (for price comparison and competitor analysis), investment analysis (for market data acquisition), cryptocurrency (for tracking prices and market trends), and marketing (for lead generation and market research). The market is segmented by both pricing model (pay-to-use and free-to-use) and application, reflecting the diverse needs and budgets of users. Leading players such as Import.io, HelpSystems, and Scrapinghub are driving innovation and competition, pushing the boundaries of scraping capabilities and user experience. The market is geographically diverse, with strong growth anticipated in North America, Europe, and Asia Pacific, fueled by the high concentration of tech-savvy businesses and data-driven decision-making cultures in these regions. Future growth will be influenced by factors such as increasing data regulations, advancements in AI-powered scraping tools, and evolving website structures that require sophisticated techniques for data extraction. The continued expansion of e-commerce, financial technology, and marketing automation will further propel the demand for effective and efficient web scraping tools in the coming years. The free-to-use segment offers entry points for smaller businesses and individuals, while the pay-to-use segment caters to larger enterprises requiring more advanced features and scalability. Regional differences reflect the varying levels of digital maturity and technological adoption across different economies. North America, with its established tech sector and strong investment in data analytics, is likely to maintain a significant market share. However, Asia-Pacific is poised for rapid growth, driven by the burgeoning e-commerce sector and increasing adoption of data-driven strategies in developing economies. The market will see ongoing innovation in areas such as machine learning integration to improve data accuracy and efficiency, as well as enhanced security features to address concerns around ethical scraping and compliance.

The OTA, booking websites have a ton of information like pricing, promotions, occupancy reviews, etc about hotels. Our data as a service offering helps our customers get this data through web scraping. The data is refreshed every day and delivered to our customers via Amazon S3, The most common use cases are competitive intelligence and marketing spend optimization.

Global AI price tracking tools market size was $2.79 billion in 2024 & is projected to reach $7.30 million by 2034, CAGR of 12.80% from 2025 to 2034.

The global web scraper software market, valued at $7241.5 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid expansion of e-commerce, big data analytics, and the increasing need for real-time data across various sectors, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers. The rising demand for automated data extraction from websites for market research, price comparison, lead generation, and competitive analysis is significantly boosting market adoption. Furthermore, advancements in AI and machine learning are enhancing the capabilities of web scrapers, enabling more efficient and accurate data retrieval. The diverse application segments, including retail & e-commerce, advertising & media, finance, and real estate, all contribute to the market's expansive potential. While challenges such as website structure changes and legal constraints related to data scraping exist, the overall market outlook remains positive. The increasing sophistication of web scraping tools and the development of robust solutions that address legal and ethical concerns are mitigating these restraints. The market segmentation reveals a diverse landscape. General-purpose web scrapers cater to a broad user base, while focused scrapers target specific data types and websites. Incremental scrapers efficiently update existing datasets, and deep web scrapers access data beyond standard search engines. The application-based segmentation underscores the versatility of the technology, with e-commerce and advertising and media sectors being significant contributors. Leading players like Apify, Import.io, and Octoparse are driving innovation and competition, contributing to a robust and evolving market. Regional analysis suggests significant market presence across North America and Europe, followed by a growing presence in the Asia-Pacific region. The continued development of robust, ethical, and user-friendly web scraping solutions will be key to unlocking the full potential of this rapidly expanding market.

Context This dataset was created by web-scraping the house-announcements website Immobiliare Beautiful Soup. It serves as a good exercise in data cleaning and prediction. The goal is to predict house prices using the variables available. It contains both cleaned and raw data (for data cleaning training). My GitHub repository also contains the scripts to rerun automatically scraping and adding the newly available announcements. The dataset will be updated over time so that time-series analysis will be possible in the future.

Context 1 rooms 2 m2 3 bathrooms 4 floor 5 condominium_expenses (in euros) 6 date --> date the announcement is uploaded online 7 contract --> type of contract 8 typology --> type of property 9 total_floors --> floor level 10 availability 11 other_features --> other features written in the announcement in Italian (to be processed) 12 price --> target variable 13 year_of_build 14 condition 15 air_conditioning 16 energy_efficiency --> check here 17 city 18 neighborhood 19 car_parking 20 energy_performance_building 21 housing units 22 start_end_works 23 current_building_use 24 energy_certification 25 co2_emissions 26 elevator 27 floor_level 28 heating_centralized 29 heating_radiator 30 heating_gas 31 air_conditiong_centralized 32 air_conditioning_heat 33 renewable_energy_performance_index_KWh/m2

Dataset obtenido mediante técnicas de scraping para la asignatura de Tipología y ciclo de vida de los datos de la UOC

Los datos se han obtenido del sitio web preciosmundi.com propiedad de Víctor Rodríguez Obensa.

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Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Scraping Grader Market Outlook

The global market size for the Scraping Grader market was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a CAGR of about 12.5% during the forecast period. This growth is primarily driven by the increasing need for accurate and timely data extraction across various industries.

One of the main growth factors for the Scraping Grader market is the escalating demand for data-driven decision-making in business operations. As industries grow more competitive, the need for real-time data extraction to inform strategic decisions has become imperative. This has led to an increased adoption of scraping and grading technologies that can efficiently process large volumes of data from various sources. Both large enterprises and SMEs are investing significantly in these technologies to stay ahead of the curve and maintain a competitive edge.

Another significant driver is the rise in digital transformation across industries. Companies are increasingly leveraging web scraping tools to gather critical market insights, conduct competitive analysis, and monitor pricing strategies. The exponential growth of e-commerce and online businesses has further augmented the demand for scraping graders, as these enterprises need to continuously analyze market trends, customer preferences, and competitor activities. The integration of advanced technologies like AI and machine learning into scraping solutions has enhanced their efficiency and accuracy, making them indispensable tools for modern businesses.

The expanding applications of scraping graders in diverse sectors such as BFSI, healthcare, and retail is also a noteworthy growth factor. In the financial sector, for instance, scraping graders are used for market analysis, monitoring stock prices, and collecting financial news. Similarly, in healthcare, these tools help in gathering patient data, tracking pharmaceutical prices, and monitoring market trends. Retailers use scraping graders for price monitoring, inventory management, and understanding customer behavior. This wide range of applications across multiple sectors is significantly boosting the demand for scraping grader solutions.

From a regional perspective, North America holds a dominant position in the Scraping Grader market due to the early adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation in countries like China and India, growing e-commerce activities, and increasing investments in data-driven technologies. Europe and Latin America are also expected to experience substantial growth, driven by the rising demand for efficient data extraction solutions and the growing awareness of the benefits of data-driven decision-making.

Component Analysis

The Scraping Grader market by component is segmented into software, hardware, and services. The software segment dominates the market, accounting for a significant share due to the increasing adoption of advanced scraping tools and solutions. These software solutions offer a wide range of functionalities, including data extraction, processing, and analysis, which are essential for businesses to make informed decisions. The integration of AI and machine learning algorithms in these software solutions has further enhanced their efficiency and accuracy, making them highly sought after in the market.

The hardware segment, although smaller in comparison to software, plays a crucial role in the overall functioning of scraping grader solutions. High-performance hardware is required to support the complex algorithms and large-scale data processing needs of modern scraping tools. With advancements in computing technology, the hardware segment is expected to grow steadily, driven by the need for more powerful and efficient systems to handle the increasing volumes of data.

The services segment encompasses a range of offerings, including consulting, implementation, training, and support services. These services are critical for the successful deployment and operation of scraping grader solutions. Consulting services help organizations identify the right tools and strategies for their specific needs, while implementation services ensure seamless integration with existing systems. Training and support services are essential for maximizing the benefits of these solutions by ensuring that users are well-versed in t

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.

En el presente proyecto, hemos creado un web crawler capaz de navegar por la web del supermercado Carrefour, extrayendo los datos de los diferentes productos que allí se exponen y sus atributos principales (precios, ofertas y promociones). Este ejercicio tiene como finalidad entender y poner en práctica los conceptos aprendidos en la asignatura de Tipología y Ciclo de Vida de los Datos, asignatura perteneciente al Máster en Ciencia de los Datos de la Universitat Oberta de Catalunya, para realizar un Web Scraping.

El dataset CarrefourDailyPricing está formado por una lista con los productos del supermercado Carrefour, guardando los datos más importantes como: la categoría del producto, su precio actual, precio por kilogramo, ofertas y promociones.

El dataset CarrefourDailyPricing contiene los siguientes atributos:

•Categoría: Sección del supermercado a la que pertenece un producto.

•Descripción: Nombre que tiene asociado elproducto en el supermercado.

•Precio: Precio de venta actual.

•Precio Medida: Unidad de medida (Kg/L/ud/..)

•Precio Previo: anterior a la oferta.

•Precio Oferta: Precio de venta durante la oferta.

•Promociones: Promociones activas para cada producto.

•Enlace: Enlace donde se puede encontrar el producto en cuestión.

La araña está programada para actualizar el dataset una vez al día, a las 8:00hrs, generando un csv nuevocogiendo los atributos ya mencionados.

Este conjunto de datos busca recoger diariamente información de interés sobre cada producto en el supermercado Carrefour. Durante la elaboración de este proyecto, no solo buscamos la extracción de datos, sino también poder llevar un registro donde comparar las subidas y bajadas de precios en un período de tiempo determinado. Por esa razón, hemos programado la araña para que recorra la web diariamente, y así poder remarcar los productos en oferta, cada cuánto tiempo ocurren y su evolución en el tiempo. También, serviría para estudiar el impacto que tienen sobre la economía las crisis como la que vivimos actualmente del covid.