Foundations of AI Native Data Engineering

This course introduces the foundational shift from traditional data engineering to AI-native data engineering. Learners reframe data platforms and pipelines as intelligent, production-grade assets that power analytics, machine learning, generative AI systems, semantic search, RAG workflows, and AI-powered assistants. This course explains how AI workloads consume data differently, introduces embeddings and vector retrieval as core primitives, demystifies LLMs as technical systems, and helps learners connect familiar data engineering skills to modern AI data lifecycles and AI-native architectures. The updated Course 1 design also emphasizes that production AI-native systems require ownership boundaries and collaboration across data engineering, ML/AI engineering, application engineering, platform engineering, security/governance, and product teams. By the end of the course, learners understand how modern AI systems are built end to end, how data engineering enables them, and how to redesign legacy pipelines to support semantic search, RAG, and AI-powered workflows.