How do you compare normalization approaches?

1 Normalization levels

Normalization is based on a set of rules or principles, called normal forms, that define how to organize data in tables and columns. The most common normal forms are first, second, and third normal form (1NF, 2NF, and 3NF), which aim to eliminate repeating groups, partial dependencies, and transitive dependencies, respectively. Higher normal forms, such as Boyce-Codd normal form (BCNF), fourth normal form (4NF), and fifth normal form (5NF), address more complex cases of data anomalies and dependencies. To compare normalization approaches, you need to understand the criteria and implications of each normal form and how they affect the database design and functionality.

2 Denormalization techniques

Denormalization is the opposite of normalization, as it involves adding redundant data or combining tables to reduce the number of joins and improve query efficiency. Denormalization techniques include creating summary tables, adding derived or calculated columns, duplicating data across tables, and using denormalized data structures, such as arrays or JSON objects. To compare denormalization approaches, you need to consider the trade-offs between query speed and data quality, as well as the impact on the database maintenance and scalability.

3 Comparison factors

Comparing normalization and denormalization approaches requires evaluating several factors that affect the database performance and usability. Data volume and complexity should be taken into account, as normalization is more suitable for large and complex data sets, while denormalization is better for small and simple data sets. Additionally, query frequency and type should be considered; normalization is more suitable for frequent updates/inserts, while denormalization is better for frequent reads/analytical queries. Data quality and integrity should also be taken into account, as normalization enforces validation/normalization rules, while denormalization sacrifices data quality for query performance. Finally, database maintenance and scalability should be evaluated; normalization simplifies data management/modification, while denormalization complicates maintenance/scalability.

4 Comparison examples

To illustrate how to compare normalization and denormalization approaches, let's consider some examples of database scenarios and how they can be designed differently. For instance, an online store selling products from different suppliers could use a normalized approach with separate tables for products, suppliers, and orders, linked via foreign keys. Alternatively, a denormalized approach could employ a single table for products with supplier name and order details as additional columns or nested objects. Similarly, a social media platform storing user profiles and posts could use separate tables for users, posts, and comments connected by foreign keys or a single table with posts and comments as arrays or JSON objects within each user document. Finally, a reporting system analyzing sales data from different regions and categories could use separate tables for sales, regions, and categories linked by foreign keys or a summary table for sales with region name and category name as additional columns or calculated values.

5 Comparison tools

To compare normalization and denormalization approaches, you can use various tools and methods to measure and compare database performance and usability. Data modeling tools, such as ERwin, MySQL Workbench, and Lucidchart, can help design and visualize the database schema and relationships using different levels of normalization or denormalization. Benchmarking tools, such as HammerDB, pgbench, and JMeter, can help test and compare the database performance and efficiency using different queries and scenarios. Furthermore, data quality tools, such as Talend Data Quality, Informatica Data Quality, and DataCleaner, can assess and compare the data quality and integrity using different metrics and rules.

6 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?