AI-Ready Data Engineering: Pipelines for RAG & Agents

Everyone is building AI features. Almost no one is building the data systems that make them actually work. That gap is the data engineer's to own — and this course teaches you exactly how. Across 25 modules and 138 lessons you build the pipelines behind real AI: retrieval, RAG, agents, and governed text-to-SQL — not toy demos, but the incremental, evaluated, secured, cost-controlled systems that survive production. You follow one engineer, Maya, whose mandate is "make our data AI-ready," and you finish by shipping the capstone — AskTheData, an end-to-end AI data platform — yourself. What makes this course different: Data-engineering-first, not prompt-first. Embeddings, chunking, ingestion freshness, data contracts, vector stores, hybrid search — the substrate AI runs on, built properly. Code-first with a lab every module. Every module ships runnable code and a hands-on lab; over 40% of slides are real code or demos. Production war stories. Every hard idea lands with a story and an analogy, and real failure drills show you exactly how these systems break — and how to stop them. Governed text-to-SQL done right. Semantic layers on Snowflake Cortex Analyst and Databricks Genie, with an evaluation harness that proves accuracy instead of hoping for it. Agents fed safely. Context engineering, the Model Context Protocol (MCP), durable memory, and access-aware retrieval — the parts most "agent" courses skip. Evaluate, observe, cost-control. An eval + observability harness that gates every change, plus real cost benchmarks — the discipline that separates a demo from a platform. What you'll build, module by module: embeddings demystified, ingestion + freshness + data contracts for AI, document parsing of the messy real world, chunking strategies that