Designing Your Database — Practical Fundamentals

Overview

This concise, practical guide focuses on the fundamentals of logical relational database design. It walks you through recognising entities in forms and spreadsheets, selecting stable primary keys, placing foreign keys to express relationships, and structuring tables to reduce redundancy and support reliable queries. Clear explanations are paired with hands-on exercises so you can test designs before implementation and avoid common redesign costs.

What you will learn

• How to identify entities and convert repeating groups or multi-valued fields into normalized tables.
• How to choose primary keys and use foreign keys to maintain referential integrity across tables.
• How to model one-to-many, many-to-many (using junction tables), and one-to-one relationships effectively.
• When to use simple UI dropdowns versus creating lookup tables for evolving value sets.
• How to sketch and use ER diagrams to validate logical designs before building them.

Core concepts, concisely explained

Relationships between tables

The guide explains how correct relationship modelling simplifies queries and updates. It covers parent/child patterns, implementing many-to-many relationships with linking tables, and recognising cases where a single table is a better fit than an artificial split.

Primary and foreign keys

Learn practical key selection—why stable surrogate keys often outperform fragile natural keys, how to avoid unnecessary compound keys, and how foreign keys enforce consistency and enable efficient joins.

Identifying tables from forms and spreadsheets

Use pragmatic rules to spot separate entities: repeating records, multi-value fields, and sets of columns that repeat per record. Guidance centres on maintainability—when splitting data improves update logic, reporting, and long-term extensibility.

Dropdowns versus lookup tables

The workbook helps you weigh simplicity against scalability. For small, stable lists a UI dropdown suffices; for growing vocabularies or values that need attributes or relationships, a lookup table is the right choice.

Logical data structures and ER models

ER diagrams are presented as low-effort validation tools to test assumptions, expose redundancy, and surface normalization decisions that reduce anomalies and future rework.

How to use this workbook

Read the short theory notes, then apply the guided exercises: sketch ER diagrams, assign primary and foreign keys, and refactor spreadsheet examples into normalized tables. Iterative paper or whiteboard design is encouraged before any implementation.

Exercises and suggested project

Practice tasks guide you through entity classification, key assignment, and relationship mapping. A recommended project ties concepts together—designing a simple music collection model (entities such as Artist, Album, Track, Genre), deciding lookup strategies, and creating sample records to validate joins and queries.

Who will benefit

Ideal for beginners and practitioners refreshing fundamentals: students, analysts, hobbyists, and developers who convert paper or spreadsheet sources into relational models. It also works well as a concise classroom or workshop resource for instructors teaching logical modelling and normalization.

Why this matters

Good logical design reduces redundancy, prevents data anomalies, and makes systems easier to extend and query. Following a step-by-step approach—identify entities, choose keys, map relationships, and validate—helps build databases that are robust, performant, and maintainable.

Tips for further learning

• Convert everyday spreadsheets into ER diagrams and compare query complexity before and after normalization.
• Implement lookup sets both as UI dropdowns and as tables to evaluate maintenance trade-offs.
• Practice normalizing denormalized datasets to see how updates, storage, and queries are affected.