AWS Glue for Data Quality in Generative AI Pipelines

Generative AI models are only as good as the data used to feed them, yet maintaining clean and structured data at scale remains a massive challenge. This text-based course guides you through utilizing AWS Glue to establish robust data quality pipelines specifically tailored for generative AI systems. You will start by mastering the foundational concepts of data preparation, schema enforcement, and data quality rules. By reading through practical, step-by-step written explanations and structured code examples, you will learn how to prepare datasets for foundation model readiness and modern retrieval-augmented generation (RAG) workflows. What you'll learn: Understand the core principles of data quality and how it directly impacts generative AI performance; Configure AWS Glue Data Quality rules to automatically detect, validate, and quarantine anomalous data; Apply schema enforcement and evolution strategies to prevent pipeline failures; Implement batch processing workflows to clean large-scale datasets for model training; Integrate structured data quality checks with modern vector databases and RAG architectures. The course begins with essential terminology and the fundamentals of data pipeline architecture before moving into configuration patterns and production-ready AWS Glue scripts. You will progress from basic schema validation to advanced quality monitoring setups. This course is designed for beginners in data engineering and AI development, requiring no prior experience with AWS Glue or complex machine learning infrastructure. Start reading today to build reliable, high-quality data pipelines that power smarter generative AI applications.