SQL and Vector Search Tuning for Modern AI Databases

As AI applications and relational databases grow, slow queries and inefficient data retrieval can stall your systems. Mastering the fundamentals of database optimization is essential to keep your applications running at peak performance. This text-based course guides you from database basics to modern tuning techniques, helping you balance speed and accuracy in both traditional relational databases and modern vector search engines. What you'll learn: - Understand foundational database concepts, query execution plans, and indexing strategies. - Analyze and optimize SQL queries to reduce latency and resource consumption. - Explore vector search fundamentals, including distance metrics like cosine similarity and Euclidean distance. - Configure vector database parameters such as HNSW and IVF index settings for optimal recall and speed. - Apply tuning strategies to Retrieval-Augmented Generation (RAG) pipelines to improve AI response times. - Practice identifying performance bottlenecks through written scenario-based exercises. You will start with core database terminology and traditional SQL optimization before transitioning into the modern landscape of vector databases and similarity search parameters. This course is designed for aspiring database administrators, software developers, and AI enthusiasts, requiring no prior performance-tuning experience. Start reading today to build faster, more efficient data retrieval systems.