Generative AI for Data Engineering and Pipeline Automation

The landscape of data engineering is shifting as artificial intelligence introduces new ways to design, build, and maintain robust data systems. This course provides a clear path for understanding how generative AI can be applied to core data tasks to increase efficiency and ensure high-quality delivery. You will learn how to transform traditional data workflows by incorporating AI-driven automation into your daily processes. Through written explanations and practical code examples, you will gain the skills needed to stay competitive in a rapidly evolving field. What you'll learn: - Understand the fundamental concepts of generative AI and its specific role in data architecture - Apply prompt engineering techniques to generate, debug, and optimize complex SQL and Python code - Build automated data pipelines that use AI for schema mapping and metadata management - Implement vector databases and retrieval-augmented generation (RAG) patterns for modern data applications - Practice data quality validation and synthetic data generation using AI-driven workflows - Master the integration of AI assistants into data orchestration and infrastructure-as-code tasks The course begins with essential terminology and foundational definitions before moving into text-based exercises that demonstrate how to apply these tools to real-world data engineering scenarios. You will read through detailed logic walkthroughs and analyze code snippets to build a deep understanding of the subject. This course is designed for beginners and aspiring data professionals who want to modernize their skillset; no prior experience with artificial intelligence is required. Start reading today to evolve your data engineering practice with generative AI.