Generative AI for Data Engineering

As data volumes grow, modern data engineers must learn to leverage generative AI to automate workflows, optimize queries, and build smarter pipelines. This text-based course bridges the gap between traditional data engineering and modern artificial intelligence, helping you work faster and smarter. You will transition from manual pipeline creation to AI-assisted engineering, learning how to use large language models to write code, document schemas, and troubleshoot errors. By understanding the foundational principles of generative AI, you will be able to integrate these tools seamlessly into your daily data workflows. What you'll learn: • Understand foundational generative AI concepts, including prompt engineering and large language models for code generation. • Apply AI assistance to write, optimize, and debug SQL queries and data pipeline scripts. • Design automated metadata documentation and data quality validation checks using generative models. • Configure retrieval-augmented generation (RAG) pipelines and work with vector databases for unstructured data. • Practice translating natural language requirements into functional data integration workflows. • Implement best practices for security, privacy, and bias mitigation when using AI tools with data. The course begins with essential terminology, outlining how generative models function and their specific utility in data management. From there, you will progress through practical text-based guides and code examples that demonstrate AI-driven schema design, pipeline generation, and modern vector database integration. This course is designed for aspiring and junior data engineers, analysts, and database administrators who want to integrate artificial intelligence into their engineering toolkit. No prior experience with generative AI is required. Start reading today to elevate your data engineering skills with the power of generative AI.