Everyone loves to overcomplicate PDF scraping. Ask around, and you’ll hear the same story: you need expensive enterprise software, specialized parsing libraries, or even a degree in document engineering. The truth? None of that is necessary. In the last year alone, I’ve scraped over 100,000 PDFs—ranging from research papers and government reports to corporate filings—without breaking the bank or losing my sanity. What I discovered is that most developers are wasting time on clunky workflows, overpriced tools, and assumptions that simply aren’t true. PDF scraping isn’t rocket science—it’s about understanding how to access data reliably and extract it efficiently. This article breaks down the myths, exposes the common mistakes, and shows you how to build a scalable scraping pipeline with just a few lines of Python and the right strategy.