دانلود کتاب مهندسی داده با هوش مصنوعی مولد و عامل‌گرا در AWS – ایجاد یک روش داده‌افزوده‌شده با هوش مصنوعی برای سازمان

Every data engineering book on the market became obsolete the moment generative AI dropped. Going forward, technical books need to include how generative and agentic AI have changed their practice. But not everything changed. The tools and strategies that store, transform, and serve data are not being replaced. They are being enhanced. I didn’t want to write a book on just the things that changed, leaving out the foundational aspects that are still required in a modern data strategy. I didn’t want to write a book about all the things that stayed the same, because people have already seen those ideas. That didn’t tell the whole story. I concluded that I needed to do both—combine the state of the art of modern data platform strategy with the new capabilities and enhancements of generative and agentic AI. AI is a great tool, but it’s a double-edged sword. It helps you scale your impact, but it can also rob you of agency (no pun intended). In chess, they have a term for less experienced players called “woodpushers.” A woodpusher makes moves without a strategy or understanding the deeper “why.” As AI takes on more responsibility, the risk is that our knowledge and understanding of technology becomes more abstracted and superficial. I address that challenge head-on with a sizable amount of ink devoted to the theory and fundamentals of data and data engineering. In many cases, this includes the historical context and origin and how that technology progressed over time. It would take you years of senior level professional experience to accumulate the knowledge I pulled together in a book that might take you a weekend to read.