Generative AI Foundations for Data Engineers

Data engineering is evolving rapidly as artificial intelligence transforms how we process, clean, and enrich data. This text-based course introduces you to the essential intersection of generative AI and data engineering, showing you how to enhance traditional pipelines with intelligent automation. You will transition from standard data processing to designing AI-assisted data workflows. By understanding how to interact with large language models, structure data for AI consumption, and implement vector storage, you will be prepared to build modern, intelligent data infrastructure. What you'll learn: - Understand foundational generative AI concepts, large language models, and their specific applications in data engineering workflows. - Apply prompt engineering techniques to automate data cleaning, parsing, and metadata generation. - Configure vector databases and design Retrieval-Augmented Generation (RAG) patterns for data retrieval. - Integrate generative AI APIs into Python-based data pipelines for automated enrichment and transformation. - Implement basic LLMOps practices to monitor, evaluate, and secure AI-driven data processes. You will start with core definitions and the basics of language models before moving into hands-on pipeline integration, prompt design, and vector database management. Through clear written explanations and practical code examples, you will learn to build robust, AI-enhanced data architectures. This course is designed for aspiring and practicing data engineers, database administrators, and software developers who want to integrate AI into their data workflows. No prior experience with generative AI is required, though a basic understanding of Python and database concepts is helpful. Start reading today to adapt your data engineering skills for the era of artificial intelligence.