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Data Acquisition and Integration - Scraper/Crawler/Fetcher

Overview​

This section outlines the requirements for the data acquisition and integration component, which we'll refer to as the "scraper" or "crawler/fetcher". The primary function of this component is to retrieve data from various external sources and store it within our database.

Data Sources​

The scraper must be able to handle a variety of data sources, including but not limited to:

  • Web Links (URLs): Crawling websites to extract data from HTML content.

  • Spreadsheets: Importing data from files like CSV or Excel.

  • APIs: Interacting with web APIs to retrieve structured data.

  • Other File Formats: Supporting additional file types as needed (e.g., JSON, XML).

  • Addicinalt, any file that is uploaded we should be able to copy any metadata that come with it

  • AI in adition to scraping data we should also use AI to capture data and metadata that is not obvius, the type only AI can get from anilixing images, videos, large quantities of test, etc.

Data Integration​

Once the data is retrieved, it needs to be appropriately integrated into our system. This includes:

  • Database Storage: Storing the data in the designated database tables.
  • User/Client Association: If the data acquisition is initiated by a user or client, the data should be associated with their specific account.
  • Library Association: If the data is uploaded by us (administrators) or a designated "library owner," it will be associated with a specific library.
    • Library as a Client: We will further discuss and clarify if a library should be treated as a special type of client or if a separate entity is necessary.

Key Considerations​

  • Scalability: The scraper should be designed to handle large volumes of data and diverse sources.
  • Error Handling: Robust error handling and reporting are essential for data consistency and reliability.
  • Authentication: Secure access and handling of authentication credentials for APIs and other protected sources.
  • Data Validation: mechanisms to ensure the quality and validity of the scraped data.
  • Rate limiting should be a consideration, along with how to go around it if possible and legal.