Learn how AI workloads are changing data engineering and why traditional BI-focused pipelines often need redesign to support semantic search, RAG, LLM applications, embeddings, and agentic consumers. You’ll compare ETL and AI-native pipelines, identify architecture and governance gaps, and begin a course project by defining an AI use case, mapping requirements to platform components, and clarifying ownership boundaries.

This module introduces core AI concepts from a data engineering perspective, helping you distinguish AI, machine learning, deep learning, generative AI, and LLM-based systems. You will also learn how key AI stack components such as data preparation, embeddings, vector databases, retrieval systems, RAG, and serving interfaces work together in real applications.

Learn how the AI data lifecycle extends data engineering beyond traditional analytics into feature engineering, embeddings, retrieval, inference, evaluation, monitoring, and governance. You’ll examine how AI data engineers design AI-ready pipelines, identify drift and leakage risks, define monitoring signals, and coordinate ownership across teams to support reliable, explainable, and governed AI systems.

Learn how large language models shape data engineering decisions around prompting, context management, hosting, cost, latency, scaling, fine-tuning, and retrieval-augmented generation. You’ll focus on practical architecture, governance, and production tradeoffs rather than model research, and create project artifacts to support LLM integration decisions.

Learn how retrieval-augmented generation (RAG) pipelines work and how data engineers design, support, and troubleshoot them in AI-native systems. You will examine the full RAG workflow—from source preparation and chunking to retrieval, prompt assembly, evaluation, governance, and failure diagnosis—and compare when RAG, fine-tuning, or both are the right choice.

Apply the concepts from Course 1 to design or build a semantic search workflow using AI-native data architecture patterns. You will work with corpus preparation, embeddings, vector retrieval, metadata, governance, and optional RAG-style context assembly, then package your work into a final project-ready semantic search or RAG design.

Wrap up Course 1 by connecting what you built to portfolio-ready evidence, professional practice, and the next step in the certificate. In this short bookend module, you’ll review your final project artifacts, reinforce the AI-native data engineering mindset, and create a practical plan for refining your work and preparing for Course 2.