Practical Relational Data Modeling with SQL

Design Better Databases. Protect Data Integrity. Make Schema Changes with Confidence.

Writing SQL is one skill. Designing a relational database that remains accurate, efficient, and maintainable as an application grows is another.

Practical Relational Data Modeling with SQL teaches developers how to turn business requirements into reliable database schemas, enforce meaningful constraints, and evolve existing structures without compromising critical data.

Using PostgreSQL, practical SQL examples, and a progressive order management project, Cal Vale explains not only how to implement relational designs, but why particular approaches work and when alternatives make sense.

What You'll Learn

• Translate requirements into entities, attributes, keys, relationships, and logical data models.
• Apply normalization, functional dependencies, and thoughtful denormalization.
• Protect data with primary keys, foreign keys, UNIQUE, NOT NULL, and CHECK constraints.
• Model complex relationships, hierarchies, orders, payments, and inventory.
• Choose useful indexes, interpret query plans, and understand performance tradeoffs.
• Handle transactions, concurrency, isolation levels, and common data anomalies.
• Plan safe migrations, schema refactoring, and backward-compatible changes.
• Test constraints, verify database behavior, and assess production readiness.

Build and Evolve a Practical PostgreSQL Database

Follow a connected order management example involving customers, products, orders, order items, payments, and inventory. The capstone brings together schema design, SQL implementation, indexing, transactions, migrations, and integrity checks. Four appendices provide SQL references, design patterns, checklists, and project scripts.

Who Should Read This Book?

Ideal for software developers, backend engineers, database developers, data engineers, and SQL learners ready to move beyond basic queries. The PostgreSQL examples are practical, while the underlying relational principles apply across database systems.

Build schemas that represent business rules accurately, defend against invalid data, and remain easier to maintain as requirements change.