Key Datasets and Databases for Web Scraping in 2025

Web scraping has become an essential part of data operations in 2025. Companies, researchers, and developers depend on it to extract information from websites. Access to the right datasets and databases streamlines projects and saves time.

Powerful data collection tools simplify the extraction of structured information from websites. Many projects utilize public domain datasets to train models, test algorithms, and analyze trends. This year, several resources have emerged as particularly useful for efficient and accurate data collection.

Wikipedia now provides a structured dataset through Kaggle, including English and French content like article summaries and infoboxes. Non-text content is removed, facilitating easier handling. This dataset is popular for AI projects and research, offering a large, ready-to-use collection without manual scraping.

Harvard University has released a massive dataset of nearly one million public-domain books, covering diverse genres, languages, and time periods. This dataset is ideal for AI, language research, and natural language processing. Previously, access to such extensive datasets was mainly limited to major tech companies.

Bright Data offers pre-built datasets from websites like Amazon, LinkedIn, Pinterest, and Redfin. These datasets cover various categories, including product prices, real estate listings, and sports statistics. The marketplace charges a subscription fee starting at $250 per month for 100,000 records, providing businesses with quick data access without building a scraper from scratch.

Datarade functions as a marketplace for web scraping datasets. Users can preview samples before purchasing to ensure the data meets their needs. The platform offers hundreds of datasets across different fields, including e-commerce, finance, and marketing. It's beneficial for anyone needing high-quality data for analysis or AI projects.