What are the most common normalization mistakes to avoid?

2 Mistake 2: Under-normalizing

Under-normalizing occurs when too few levels of normalization are applied to data, leading to a small number of tables with many columns and few joins. This can cause issues such as increased redundancy and duplication of data, reduced data integrity and consistency due to more update and delete anomalies, and increased storage space and memory usage due to more data. To avoid under-normalizing, one should adhere to the basic rules of normalization, including eliminating repeating groups of data by creating separate tables for each entity, eliminating partial dependencies by creating separate tables for each attribute that depends on only part of the primary key, and eliminating transitive dependencies by creating separate tables for each attribute that depends on another attribute that is not part of the primary key. By following these rules, one can ensure their data is properly normalized.

3 Mistake 3: Choosing the wrong primary key

The primary key is an essential element for enforcing data integrity and establishing relationships between tables. However, selecting the wrong primary key can cause issues such as violating the uniqueness constraint with duplicate values, the stability constraint with changing values, and the simplicity constraint with overly long or complex values. To avoid these problems, it is recommended to use a single column as the primary key when possible, or a surrogate key if a natural key is not unique, stable, or simple. Additionally, an integer or numeric data type should be used instead of a string or date data type to improve performance and avoid formatting issues.

4 Mistake 4: Ignoring data types and constraints

Data types and constraints are essential for defining the structure and rules of your data, ensuring it is valid, consistent, and secure. However, neglecting data types and constraints can lead to issues such as storing incompatible or inaccurate data in the columns, allowing null or empty values in columns that should not have them, or allowing invalid or unauthorized operations on the data. To prevent this, you should adhere to these best practices: select the appropriate data type for each column based on its nature and range of the data; use constraints such as not null, unique, check, default, and foreign key to enforce the rules and relationships of the data; and employ triggers - actions that are executed automatically when a certain event occurs - to implement custom logic and validations on the data.