Scaling Data Pipelines with Generative AI

Data engineering is evolving rapidly as generative AI introduces new paradigms for processing, structuring, and retrieving information. To stay ahead, data professionals must learn how to integrate these intelligent models directly into their data pipelines. This text-only course guides you through the core concepts of Generative AI specifically tailored for data engineering, helping you transition from traditional data pipeline design to building modern, AI-augmented data architectures. What you'll learn: - Understand the foundational concepts of generative AI and its role in modern data architecture - Design and implement retrieval-augmented generation (RAG) patterns within data pipelines - Configure and optimize vector databases for storing and retrieving high-dimensional embeddings - Automate metadata extraction, data cleaning, and schema mapping using large language models - Apply basic MLOps concepts to monitor and maintain generative AI workflows at scale The curriculum begins with essential terminology and foundational AI concepts before moving step-by-step into practical pipeline integration, vector storage, and scaling strategies. This course is designed for beginner data engineers and database professionals looking to expand their skillset into AI, requiring no prior generative AI experience. Start reading today to prepare your data infrastructure for the next generation of AI applications.