What you'll learn

Data engineers often spend significant time writing repetitive boilerplate code, troubleshooting pipeline failures, and translating business requirements into technical implementations. In this course, Building Data Pipelines with Generative AI, you will gain the ability to use large language models (LLMs) to accelerate data engineering workflows safely and effectively. First, you will explore how to design structured prompts for generating SQL queries, dbt models, Spark jobs, and Airflow workflows from plain-language requirements. Next, you will discover how to apply advanced prompting techniques such as chain-of-thought prompting and prompt chaining to improve accuracy on complex, multi-step transformations. Finally, you will learn how to use LLMs to debug pipeline failures, interpret Spark and Airflow error messages, and build reusable troubleshooting prompts for common production issues. When you are finished with this course, you will have the skills and knowledge of generative AI-assisted data engineering needed to accelerate pipeline development, improve troubleshooting workflows, and safely integrate LLMs into modern data engineering environments.