What is denormalization?

Denormalization is the process of purposely adding redundant, precomputed data into a previously normalized relational database to improve read performance. With this technique, a database administrator selectively reintroduces redundant data after the data structure has already been normalized. It is important to know that a denormalized database is not the same as one that has never been normalized at all.

Normalization vs. denormalization

Denormalization directly addresses a key challenge in databases: slow read and join operations. In a fully normalized setup, data is stored only once—generally in separate tables—and related through keys. To fetch combined information, the database must join these tables during queries, which can overwhelm hardware or reduce performance, especially for complex or frequent reads.

Real-world analogy

Imagine a fruit seller with two lists: one tracking in-stock fruits, another for daily prices. In a normalized database, these would be separate tables, so finding the price of an in-stock item would require looking at both lists—resulting in slower service. Denormalization is like combining these lists, offering faster answers as the information is pre-joined.

Important considerations and tradeoffs for data denormalization

One major factor to consider is whether your data needs are "read heavy" or "write heavy." Denormalized databases involve data duplication, so every data insertion or update may require modifying multiple tables—resulting in slower writes. The central tradeoff is essentially: normalization offers fast writes but slow reads, while denormalization offers fast reads but slow writes.

Real-world analogy

For example, consider a database of customer orders from an e-commerce site. If new orders come in frequently but are not often read, fast write performance (normalization) is best. But if orders are read multiple times per second (for recommendations or analytics), denormalization might be preferable for improved read speeds.

Another crucial aspect of denormalization is data consistency. Since redundant data can exist in multiple places, changes might not propagate everywhere, creating inconsistencies or "update anomalies." This means the application and database need processes to ensure consistency is maintained.

When should you denormalize a database?

Normalized databases split related data into separate tables to minimize redundancy. Queries that join information from these tables can be slow, especially as datasets grow. To improve query speed, administrators may denormalize the database by adding redundant data to a normalized design. It's essential to note that denormalization is always performed after normalization—it is not the same as skipping normalization altogether.

Database denormalization: Going beyond relational databases and SQL

Denormalization is not limited to just relational databases and SQL. It's also common in NoSQL databases, particularly document-oriented ones that underpin many content management systems. Here, denormalization helps render complex pages quickly by reducing the number of joins needed. Columnar databases, such as Apache Cassandra, also benefit from denormalized views, using high compression to balance the increased storage requirements.

Denormalization pros and cons

There are several advantages and disadvantages to denormalizing databases:

Pros

• Faster data reads thanks to reduced joins
• Simpler queries for developers
• Lower compute load during read operations

Cons

• Slower write operations since more tables need to be updated
• Increased database complexity
• Risk of data inconsistency due to redundant copies
• More storage required to hold additional data

However, advances in technology and cheaper disk and RAM have made storing redundant data less costly. As a result, denormalization has become increasingly popular in modern database design, especially where read performance is a top priority.

Denormalization in logical design

The ways denormalization is implemented can differ depending on the database management system (DBMS) vendor. Automated denormalized views are usually a premium feature offered in paid DBMS products. These systems may provide "materialized" or "indexed" views, which precompute and store results to speed query execution. For example, Microsoft SQL Server provides indexed views, while Oracle supports materialized views. Database administrators can add denormalized tables directly or use DBMS features for automated consistency management.

Denormalization in data warehousing

Denormalization plays a key role in data warehousing. Since warehouses store very large datasets and can serve many simultaneous users, minimizing slow joins is critical. Denormalizing ensures more predictable and faster read performance. For dimensional data warehouses, this approach—championed by experts like Ralph Kimball—helps accelerate business intelligence queries and real-time analytics.