How do you simplify the normalization or denormalization process?

1 Understand your data

Before you decide whether to normalize or denormalize your database, you need to understand your data and its relationships. What are the entities, attributes, and keys that define your data? How do they relate to each other? What are the functional dependencies and constraints that govern your data? What are the queries and operations that you expect to perform on your data? These questions will help you determine the level of normalization or denormalization that suits your database design. For example, if your data is highly structured, consistent, and has minimal redundancy, you may benefit from normalization. If your data is complex, heterogeneous, and requires frequent joins, you may benefit from denormalization.

2 Choose a suitable method

There are different methods and tools that can help you simplify the normalization or denormalization process. For normalization, you can use the normal forms (NFs) as guidelines to check and improve your database design. The NFs are a series of rules that define the quality and structure of your tables. The most commonly used NFs are the first (1NF), second (2NF), and third (3NF) normal forms. Each NF has a specific condition that your tables must satisfy to be in that form. For example, to be in 1NF, your tables must have no repeating groups or arrays, and each attribute must have a single value. To be in 2NF, your tables must be in 1NF, and each non-key attribute must depend on the whole primary key. To be in 3NF, your tables must be in 2NF, and each non-key attribute must depend only on the primary key. You can use these rules to identify and eliminate any anomalies or redundancies in your tables.

For denormalization, you can use the star schema or the snowflake schema as models to organize your data into fact and dimension tables. The star schema is a simple and efficient way to denormalize your data for analytical purposes. It consists of one central fact table that contains the measures or metrics of your data, and several dimension tables that contain the descriptive or categorical attributes of your data. The fact table is linked to the dimension tables by foreign keys. The snowflake schema is a variation of the star schema that normalizes some of the dimension tables to reduce redundancy and improve data quality. It consists of one fact table and multiple dimension tables that are connected by one or more levels of intermediate tables. You can use these models to design your data warehouse or data mart for faster and easier reporting and analysis.

3 Test and evaluate your results

After you have applied the normalization or denormalization method of your choice, you need to test and evaluate your results. You can use various metrics and tools to measure the performance, usability, and maintainability of your database. For example, you can use the entity relationship (ER) diagram to visualize and verify the structure and relationships of your tables. You can use the SQL queries to retrieve and manipulate your data and check for any errors or inconsistencies. You can use the execution plan to analyze the cost and efficiency of your queries and identify any bottlenecks or opportunities for improvement. You can use the data quality tools to assess and improve the accuracy, completeness, and validity of your data. You can also use the feedback from your users and stakeholders to evaluate the functionality and satisfaction of your database.

Normalization and denormalization are not mutually exclusive or fixed processes. You can combine and adjust them according to the changing requirements and objectives of your database project. By following these tips and tricks, you can simplify the normalization or denormalization process and create a database that meets your needs and expectations.

4 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?