Modern Database Engineering with SQL, NoSQL, and Vector Databases

Modern applications demand more than traditional data storage; they require intelligent systems capable of handling structured, unstructured, and high-dimensional vector data. This comprehensive written course bridges the gap between classic database management and modern AI integration. You will transition from understanding basic data concepts to designing sophisticated database architectures that power AI applications. Through clear written explanations, structural diagrams represented in text, and practical query examples, you will gain the confidence to structure, query, and optimize data for real-world software. What you'll learn: - Understand foundational database concepts, schema design, and the core differences between SQL, NoSQL, and vector storage. - Write efficient SQL queries to store, retrieve, and optimize structured relational data. - Design flexible NoSQL document schemas using MongoDB for scalable modern applications. - Implement vector databases and similarity search to handle embeddings for AI-driven semantic search. - Apply retrieval-augmented generation (RAG) patterns to connect language models with external database sources. - Configure database connections and security best practices within production-ready application workflows. The course starts with essential terminology and the fundamentals of relational database design, then progresses through NoSQL systems and modern vector databases. You will explore practical integration patterns that connect these databases with intelligent models through step-by-step written tutorials and text-based exercises. This course is designed for beginners, developers, and aspiring data engineers who want to learn database systems from scratch. No prior database administration or AI experience is required. Start building the data foundation for the next generation of intelligent software today.