Develop a web scraping capability that, based on three parameters (Companies, Individuals, Products), extracts information from the web and creates or updates records in a database. The goal is to scrape talent resumes, company details, executive information, and product details from various sources and store them in a structured database.
Parameterized Scraping: Implement a parameterized system that allows users to choose between scraping talent resumes, company details, executive information, or product details.
Web Scraping Engine: Develop a robust web scraping engine capable of extracting information from various websites, ensuring compliance with ethical scraping practices and legal considerations.
Data Storage and Database Integration: Integrate a database to store scraped data. The database should be structured to accommodate information related to talent, companies, executives, and products.
Resume Parsing for Talent: Implement a resume parsing mechanism specifically for talent scraping to extract relevant information such as skills, experience, and education.
Company and Executive Details: Extract detailed information about companies, including company details, executive profiles, and any additional relevant data.
Product Details: Gather information on products, including product details, specifications, and associated data.
Data Validation and Cleaning: Implement mechanisms for validating and cleaning scraped data to ensure accuracy and consistency in the database.
Scheduled Scraping and Updates: Set up scheduled scraping tasks to periodically update the database with fresh information. This ensures that the database stays current and reflects the latest data available on the web.
The expected outcome is a web scraping capability that, based on user-specified parameters, can: Scrape talent resumes worldwide and parse relevant information. Collect detailed information about companies, including executive details. Extract information about a wide range of products.
Web Scraping Frameworks: Use web scraping frameworks such as BeautifulSoup, Scrapy, or Selenium for extracting data from websites.
Database: Choose a database system (e.g., MySQL, MongoDB) to store and manage the scraped data.
Resume Parsing Libraries: Utilize resume parsing libraries or APIs to extract structured information from talent resumes.
Scheduled Tasks: Implement scheduled tasks using tools like Cron Jobs, Celery, or Task Scheduler for periodic scraping and updates.
Data Validation Tools: Employ data validation tools to ensure the accuracy and integrity of scraped data.
Logging and Monitoring: Set up logging and monitoring mechanisms to track the scraping process, identify potential issues, and provide insights into system performance.
Error Handling: Develop robust error-handling mechanisms to manage errors gracefully and prevent data inconsistencies.
Scalability: Design the system to be scalable, accommodating a growing volume of scraped data and an increasing number of users.
Documentation: Provide comprehensive documentation to guide users on setting up parameters, understanding the scraping process, and using the system effectively.