Choosing correct SQL data types isn't just about "fitting the data"—it's about storage efficiency, query performance, data integrity, and future-proofing your schema. Wrong types cause bloat (VARCHAR(255) everywhere), slow queries (storing dates as strings), and validation bugs (INT for prices). This guide covers data type selection, normalization fundamentals, and production schema patterns. 

Indexes are specialized data structures that dramatically accelerate data retrieval, transforming "table scan" queries (minutes) into "index seek" operations (milliseconds). Like a book's index pointing directly to page numbers, SQL indexes point directly to matching rows. This guide covers index fundamentals, types, design patterns, and optimization techniques used by production database engineers. 

Transactions ensure database operations are atomic, consistent, isolated, and durable (ACID), preventing partial updates that corrupt data. Whether transferring money between accounts or processing e-commerce orders across multiple tables, transactions guarantee "all or nothing" execution. This comprehensive guide covers transaction fundamentals, ACID properties, isolation levels, and production best practices. 

Subqueries and Common Table Expressions (CTEs) enable you to break complex problems into manageable pieces, creating readable queries that calculate intermediate results for final analysis. From simple filtering to hierarchical traversals, these techniques transform unmaintainable nested monsters into elegant, step-by-step solutions perfect for reporting, analytics, and data engineering. 

Aggregate functions transform raw rows into meaningful business metrics, turning transactional data into actionable insights for dashboards, reports, and analytics. Combined with GROUP BY and HAVING, they enable powerful summaries like monthly revenue trends, top-performing products, and customer cohort analysis. This guide covers everything from basic aggregates to production-grade reporting patterns. 

SQL constraints are database-enforced rules that automatically validate data as it's inserted, updated, or deleted, preventing invalid states before they can corrupt your data. Unlike application-level validation, constraints work at the storage layer, catching errors even when data comes from imports, APIs, or direct database access. This guide covers all major constraint types with practical examples and production schema design patterns.