Dataset Card for "BrightData/Wikipedia-Articles"

  Dataset Summary

Explore a collection of millions of Wikipedia articles with the Wikipedia dataset, comprising over 1.23M structured records and 10 data fields updated and refreshed regularly. Each entry includes all major data points such as timestamp, URLs, article titles, raw and cataloged text, images, "see also" references, external links, and a structured table of contents. For a complete list of data points, please… See the full description on the dataset page: https://huggingface.co/datasets/BrightData/Wikipedia-Articles.

The data scraping software market is experiencing robust growth, driven by the increasing demand for real-time data insights across various sectors. 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 approximately $6 billion by 2033. This growth is fueled by several key factors. Firstly, businesses are increasingly reliant on data-driven decision-making, necessitating efficient and scalable methods for data acquisition. Secondly, the proliferation of unstructured data online presents a significant opportunity for data scraping software to extract valuable insights. Finally, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of data scraping tools, enabling more sophisticated data extraction and analysis. The market is segmented by software type (cloud-based, on-premise), application (web scraping, social media scraping, e-commerce scraping), and industry (marketing & advertising, finance, research & development). Competition in the market is intense, with a diverse range of players offering varying levels of functionality and pricing. Established companies like BrightData and Zyte compete with smaller, more specialized providers such as ParseHub and ScrapingBee. The competitive landscape is characterized by continuous innovation, with companies focusing on enhancing their offerings with AI/ML capabilities, improved data accuracy, and compliance with evolving data privacy regulations. Future growth will be shaped by factors such as increasing data volume, evolving privacy regulations (like GDPR and CCPA), the demand for ethical data scraping practices, and the rising adoption of no-code/low-code platforms for data extraction. These factors are expected to drive both market expansion and a greater focus on responsible data scraping techniques.

Dataset Card for "BrightData/IMDb-Media"

  Dataset Summary

Explore feature films, TV series, episodes, mini-series, documentaries, and more with this IMDb dataset, comprising over 249K structured records and 32 data fields updated and refreshed regularly. Each entry includes all major data points such as timestamp, title, URLs, release date, IMDb rating, reviews, awards, origin, category/genre, budget, cast, director, images, videos and more. For a complete list of data… See the full description on the dataset page: https://huggingface.co/datasets/BrightData/IMDb-Media.

The no-code web scraping tool market is experiencing robust growth, driven by the increasing demand for automated data extraction across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rise of e-commerce and the need for competitive pricing intelligence necessitates efficient data collection. Secondly, the travel and hospitality industries leverage web scraping for dynamic pricing and competitor analysis. Thirdly, academic research, finance, and human resources departments utilize these tools for large-scale data analysis and trend identification. The ease of use offered by no-code platforms democratizes web scraping, eliminating the need for coding expertise, and significantly accelerating the data acquisition process. This accessibility attracts a wider user base, contributing to market expansion. The market is segmented by application (e-commerce, travel & hospitality, academic research, finance, human resources, and others) and type (text-based, cloud-based, and API-based web scrapers). While the market is competitive, with numerous players offering varying functionalities and pricing models, the continued growth in data-driven decision-making across industries assures continued expansion. Cloud-based solutions are expected to dominate due to scalability and ease of access. Future growth hinges on the development of more sophisticated no-code platforms offering enhanced features such as AI-powered data cleaning and intelligent data analysis capabilities. Geographic regions like North America and Europe currently hold significant market share, but Asia-Pacific is poised for substantial growth due to increasing digital adoption and expanding e-commerce markets. The historical period (2019-2024) likely witnessed a moderate growth rate, setting the stage for the accelerated expansion projected for the forecast period (2025-2033). Assuming a conservative CAGR of 15% for the historical period, resulting in a 2024 market size of approximately $500 million, and applying a slightly higher CAGR of 20% for the forecast period, reflects the increasing adoption and sophistication of these tools. Factors such as stringent data privacy regulations and the increasing sophistication of anti-scraping measures present potential restraints, but innovative solutions are emerging to address these challenges, including ethical data sourcing and advanced proxy management features. The ongoing integration of AI and machine learning capabilities into no-code platforms is also expected to propel market growth, enabling more sophisticated data extraction and analysis with minimal user input.