In today's digital era, data has become one of the most valuable assets for any organization. However, the usefulness of this data drastically decreases when it is disorganized, redundant, or becomes difficult to manage. This is where the concept of normalization in the design of relational databases becomes fundamental. This article presents a clear and practical guide to understanding how to structure information efficiently.
What is Normalization?
Normalization is defined as the process of organizing data within a database. This process involves creating different tables and establishing relationships between them under a set of rules aimed at protecting data integrity and making the database more flexible. The main objectives are to eliminate redundancy and manage dependencies coherently.
Dangers of Poorly Organized Data
To grasp the importance of normalization, it is essential to identify the problems it seeks to resolve:
- Redundant Data: Having to update a customer's address in multiple locations within a system can lead to conflicting information if the update is not performed everywhere. Redundancy not only takes up disk space but also complicates maintenance. Implementing a change is much simpler if the relevant information, such as an address, is stored in a single location.
- Incoherent Dependencies: Searching for an employee's salary in the customer table is irrational since the salary belongs to the employee entity. Incoherent dependencies make accessing data difficult, as the path to find it can become illogical or broken.
Normal Forms: Structure of the Process
Normalization is organized into a series of rules known as "normal forms." A database is considered to be in "first normal form" (1NF) if it adheres to the first rule, and if it conforms to the first three rules, it is in "third normal form" (3NF). Although there are additional levels of normalization, the third form is generally sufficient for practical applications.
First Normal Form (1NF): Eliminating Repeating Groups
The goal of 1NF is to ensure that multiple fields are not used in a single table to store similar data. For example, in an inventory where an item can have multiple suppliers, it is not useful to have fields like SupplierCode1 and SupplierCode2. The arrival of a third supplier would complicate the design.
- Rules for 1NF: Repeating groups must be eliminated, independent tables need to be created for each set of related data, and each one must be identified by a primary key. In this case, the ideal solution would be to have a separate table for suppliers linked to the inventory table.
Second Normal Form (2NF): Eliminating Redundant Data
2NF focuses on ensuring that all data in a record is completely dependent on the primary key and not just a part of it.
- Rules for 2NF: Separate tables should be created for sets of values applicable to multiple records and related through a foreign key. An example would be a customer's address, which could be needed in different tables such as orders, shipments, and invoices. Instead of duplicating this information, it is preferable to store it in a centralized customer table.
Third Normal Form (3NF): Eliminating Data that Does Not Depend on the Key
3NF takes the concept a step further, requiring that all fields in a record not only depend on the primary key but also do not depend on other fields.
- Rules for 3NF: Fields that do not depend on the key must be eliminated. If certain data can apply to more than one record, it is a signal to create its own table. For example, including a candidate's university address in a hiring table can lead to problems since the same university could have multiple candidates. The solution would be to create an independent table for universities and link it to the candidates' table.
Is It Always Necessary to Normalize to the Maximum?
While complying with 3NF is ideal, it is not always practical. Taking normalization to the extreme can lead to an excess of tables for simple concepts, such as cities or zip codes, which could affect system performance or server memory capacity.
A more effective strategy is to apply 3NF only to data that changes frequently. If one opts not to fully normalize, it is crucial that the application is designed to handle inconsistencies by prompting the user to verify related fields when modifying any of them.
Beyond the Third Normal Form
There are other normal forms, such as Boyce-Codd Normal Form (BCNF) and fifth normal form, but they are rarely used in conventional design practices. Not applying these advanced rules may result in a less perfect database design, but typically does not compromise its essential functionality.
Conclusion
Normalization is a key tool for developing robust, flexible, and error-free databases. Understanding its principles not only facilitates the design of more efficient and scalable systems but also ensures the quality and consistency of information in the long run.
To delve deeper into related topics, readers are invited to explore more articles on this blog.
