SQL Architecture in the AI Era

Name: SQL Architecture in the AI Era
Availability: InStock
Author: FreeAcademy

Advanced

Requires solid understanding. Complex topics.

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.

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Course Content

8 modules • 45 lessons

Course Introduction

1 lesson

1.1

SQL Architecture in the AI Era - Introduction

Module 1: SQL in 2025 - Why It Still Runs the World

8 lessons

2.1

The SQL Paradox - More AI Means More SQL

2.2

SQL Architecture in the AI Era

Course Content

Course Introduction

SQL Architecture in the AI Era - Introduction

Module 1: SQL in 2025 - Why It Still Runs the World

The SQL Paradox - More AI Means More SQL

SQL vs NoSQL vs Vector Databases

OLTP vs OLAP vs AI Workloads

Case Study - How TikTok Uses SQL

Case Study - Uber's Real-Time Architecture

Case Study - Shopify's E-Commerce at Scale

Why AI Makes SQL More Important

The Future of SQL and AI

Module 2: Core SQL Architecture Concepts

Storage Engines - How Data Lives on Disk

B-Trees and Hash Indexes

Query Execution, Planner, and Optimizer

Caching Mechanisms

Putting It Together - Query Performance

Module 3: Indexing for AI Era

B-Tree Architecture Deep Dive

Covering Indexes (Index-Only Scans)

Partial Indexes

Expression Indexes

GIN and GiST Indexes

Vector Indexes (pgvector)

Why Indexing Matters More with AI Pipelines

Module 4: Designing Robust SQL Schemas

Normalization vs Denormalization

How AI Pipelines Break Naive Schemas

JSONB and Semi-Structured Data

Soft Deletes and Audit Tables

Metadata Tables for ML Systems

Schema Design Patterns for AI

Module 5: SQL Performance for AI

Identifying Slow Query Patterns

Handling Massive Writes

Real-Time Ingestion from ML Systems

Scaling Reads with Replicas

Caching Strategies

Performance Monitoring

Module 6: AI + SQL - Vectors and Hybrid Systems

How Vector Search Actually Works

pgvector vs Dedicated Vector Databases

Hybrid Queries (Semantic + Structured)

AI Agent Memory Architecture

Production RAG Architecture

Module 7: Building an AI-Ready SQL System (Capstone)

Project Overview and Requirements

Designing the RAG Pipeline

Building the Feature Store

Storing Model Predictions and Logs

Conversation Memory System

Analytics Dashboard Queries

Deployment and Maintenance