Many organizations rely on PDFs for sharing important documents, but these files are not ideal for storing historical data due to their inability to be easily exported into workflows. To address this, data extraction algorithms convert PDFs into structured formats like JSON or CSV, which can then be exported to databases such as MySQL, PostgreSQL, and MS-SQL using tools like Python and Nanonets. The process with Python involves extracting text or tables from PDFs and then using SQLAlchemy to connect and export data to databases, while Nanonets offers a more user-friendly, no-coding-required approach, allowing users to map extracted data to database fields and automate the process. This two-step task, though complex, can be simplified with tools like Nanonets, which facilitate seamless data transfer from PDFs to popular databases.