How can you ensure a normalized, redundancy-free data model?

1 What is normalization?

Normalization is the process of organizing your data into tables and columns that minimize redundancy and dependency. Redundancy means that the same data is repeated in more than one place, which can lead to wasted space, update problems, and inconsistency. Dependency means that the value of one attribute depends on the value of another attribute, which can lead to insertion, deletion, and modification anomalies. Normalization aims to eliminate these issues by ensuring that each table has a well-defined purpose and a unique key, and that each column contains only atomic values that are directly related to the key.

2 What are functional dependencies?

Functional dependencies are the relationships between attributes in a table that determine how one attribute can be derived from another attribute. For example, if you have a table of customers with attributes such as customer ID, name, address, and phone number, you can say that the name, address, and phone number are functionally dependent on the customer ID, because each customer ID uniquely identifies one and only one set of name, address, and phone number. Functional dependencies help you to identify the key attributes of a table, and to split a table into smaller tables if there are multiple keys or partial dependencies.

3 What are normal forms?

Normal forms are the standards or criteria that a table must meet to be considered normalized. There are different levels of normal forms, from the first normal form (1NF) to the fifth normal form (5NF), each with its own rules and requirements. Generally, the higher the normal form, the more normalized the table is, and the less redundancy and dependency it has. However, achieving a higher normal form may also involve some trade-offs, such as more tables, more joins, and more complexity. Therefore, you should balance the benefits and costs of normalization according to your specific needs and goals.

4 How to use entity-relationship diagrams?

Entity-relationship diagrams (ERDs) are graphical tools that help you to visualize and document your data model. An ERD shows the entities or objects in your data model, such as customers, products, orders, etc., and the relationships or associations between them, such as one-to-one, one-to-many, or many-to-many. An ERD also shows the attributes or properties of each entity, and the keys or identifiers that distinguish them. An ERD can help you to identify the entities and relationships in your data model, and to check for any redundancy or dependency issues.

5 How to use relational schemas?

Relational schemas are textual tools that help you to define and implement your data model. A relational schema shows the name and structure of each table in your data model, along with the constraints and rules that apply to them. A relational schema can help you to translate your ERD into a database, and to specify the data types, primary keys, foreign keys, and other features of each table. A relational schema can also help you to perform queries and operations on your data model, such as creating, reading, updating, and deleting data.

6 How to use normalization algorithms?

Normalization algorithms are systematic methods that help you to normalize your data model. A normalization algorithm takes a table as an input, and applies a series of steps or rules to decompose it into smaller tables that meet a certain normal form. A normalization algorithm can help you to simplify and improve your data model, and to avoid any redundancy or dependency problems. There are different normalization algorithms for different normal forms, such as the synthesis algorithm for 3NF, or the projection-join algorithm for BCNF.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?