The article provides a comprehensive guide on gathering financial data manually and using the Bright Data Financial Data Scraper API for automation. It outlines the complexities involved in identifying and organizing financial data, such as stock prices and company performance metrics, and emphasizes the importance of understanding legal and ethical considerations in web scraping. The manual scraping process is detailed with a tutorial using Python, involving setting up a virtual environment and writing code to extract data from web pages. However, manual scraping poses challenges like dealing with CAPTCHAs and maintaining code due to frequent website updates. In contrast, the Bright Data Financial Data Scraper API simplifies the process by automating data extraction, handling proxy management, and offering scalability with minimal coding. The API provides structured, reliable data and reduces the burden of maintaining web scrapers, making it an efficient tool for financial data analysis and market research.