Data Storage with S3 and OpenSearch for Generative AI Applications

Building modern generative artificial intelligence applications requires a solid foundation in data storage, indexing, and retrieval. This text-based course guides you through the essential mechanics of storing large-scale unstructured data and querying it efficiently using vector search. You will transition from basic storage concepts to implementing advanced retrieval architectures that feed directly into large language models. By completing this course, you will understand how to manage data pipelines that connect cloud storage with high-performance search engines, enabling highly relevant search results for AI applications. What you'll learn: - Understand foundational storage concepts in S3, including bucket structures and metadata organization - Configure OpenSearch clusters for vector database operations and semantic search - Implement vector embeddings and index high-dimensional data for precise similarity matching - Apply metadata filtering techniques to refine search queries and improve retrieval accuracy - Design secure and scalable data pipelines connecting cloud storage to search engines - Practice constructing search queries that support retrieval-augmented generation patterns This course begins with core definitions and the fundamentals of object storage and search indexing. You will then progress through step-by-step written guides and configuration examples that demonstrate how to link these systems together for semantic search tasks. This course is designed for software developers, data enthusiasts, and beginners to cloud-based AI infrastructure who want to learn data retrieval patterns without needing prior experience in vector databases. Take your first step toward building intelligent, data-driven applications today.