Master database internals, indexing, and schema design for modern AI applications. Learn how SQL databases power production AI systems at TikTok, Uber, and Netflix. Build RAG systems, feature stores, and high-performance pipelines with PostgreSQL and pgvector.
SQL is not fading as AI rises. If anything, understanding how databases are structured and why they behave the way they do has become more valuable as AI systems grow more complex. This free advanced course on SQL architecture in the AI era takes you deep into the internals that power production systems at companies like TikTok, Uber, and Shopify. You will learn how storage engines, B-tree and vector indexes, query planners, and schema design decisions directly affect the performance of AI pipelines, feature stores, and retrieval-augmented generation systems.
The course is structured for developers, data engineers, and technical practitioners who already know SQL fundamentals and want to work confidently with database infrastructure behind real AI products. Through seven modules, you will progress from the core architectural concepts that explain how PostgreSQL executes and optimizes queries, to advanced indexing strategies including pgvector for semantic search, to schema patterns designed to survive the write patterns and semi-structured data that ML systems produce. The capstone module walks you through designing a complete AI-ready SQL system, including a RAG pipeline, feature store, conversation memory, and analytics layer.
This course is completely free and requires no account to start. Completing all modules and passing the final exam earns you a certificate of completion you can add to your LinkedIn profile or resume. Whether you are building AI tooling at work, studying for a data engineering role, or integrating language models into a personal project, the architectural thinking you develop here transfers directly into better, faster, and more maintainable systems.
8 modules • 45 lessons
The course covers SQL architecture from first principles through advanced AI integration, including storage engines, indexing strategies like GIN, GiST, and pgvector, schema design patterns for ML systems, query performance tuning, and a capstone project that builds a full RAG pipeline and feature store in PostgreSQL.
Yes, the entire course is free to access with no signup required. Completing all lessons and passing the final exam earns you a certificate of completion you can share on LinkedIn or include in your portfolio.
This is an advanced course and assumes you are already comfortable writing SQL queries, joins, and basic schema design. Some familiarity with how web applications or data pipelines use databases will help you get the most out of the architecture and performance modules.
The course centers on PostgreSQL and its pgvector extension for vector search. Architectural concepts are taught in a way that applies broadly to relational databases, but the indexing, JSONB, and AI integration lessons use PostgreSQL specifically.
AI systems depend heavily on relational databases for feature stores, prediction logging, conversation memory, and hybrid retrieval. This course shows you exactly how to structure and tune PostgreSQL to handle those workloads, using real patterns drawn from production AI architectures.
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