What is denormalization and how does it relate to database design?

1 What is denormalization?

Denormalization is the process of modifying a normalized database to increase its efficiency and performance for specific purposes. It usually involves adding redundant data or combining tables that were previously separated by normalization. The main goal of denormalization is to reduce the number of joins and lookups required to retrieve data from multiple tables, which can improve the speed and simplicity of queries. However, denormalization also has some trade-offs, such as increased storage space, data inconsistency, and update anomalies.

2 Why use denormalization?

The main reason to use denormalization is to optimize a database for read-intensive operations, such as reporting, analytics, or data warehousing. These operations often require complex queries that access large amounts of data from multiple tables, which can be slow and expensive in a normalized database. By denormalizing the database, the data can be stored in a more convenient and accessible format, such as a single table or a pre-computed summary. This can reduce the query time and the load on the database server, as well as simplify the query logic and code.

3 When to use denormalization?

Denormalization is not a universal solution and should be applied cautiously and selectively depending on the database and application needs and characteristics. Before denormalizing a database, factors such as query frequency and type, data volume and velocity, and consistency and quality should be taken into account. For instance, if queries are mostly read-only, simple, and predictable, denormalization can be beneficial. However, if the data is large, fast-changing, or distributed, denormalization can be difficult or unnecessary. Additionally, if the data needs to be consistent, accurate, and reliable, denormalization can create risks and complications. On the other hand, if the data can tolerate some inconsistency or duplication, denormalization can be acceptable and manageable.

5 What are the challenges of denormalization?

Denormalization is not a risk-free process and can present some challenges for database design and maintenance. Data inconsistency can occur when redundant or derived data is added, potentially resulting in errors, conflicts, or discrepancies. Data integrity can be affected by merging or summarizing tables, leading to a lack of accuracy, validity, or completeness. Additionally, denormalizing the database can compromise data security, as it can make the data more exposed and vulnerable to modifications. All of these issues can be especially concerning if the data is frequently updated, used for analysis or decision making, or sensitive or confidential.