Data Normalization vs. Denormalization Comparison

What is data normalization?

Data normalization is the process of structuring a database to enhance efficiency, maintain consistency, and eliminate redundant data. The idea is simple: break data into smaller, related tables to minimize repetition and simplify updates. By following a set of rules called “normal forms,” normalization helps prevent redundancy and maintain consistency in a database.

Example of normalized data

Imagine that you’re managing a university database. You need to track students, the courses they take, and their enrollments. Here’s how you would organize that data in a normalized table:

Students table:

Courses table:

Enrollments table (links students and courses): 

Now, imagine updating Alice’s email address in a database. Without normalization, you’d need to change her email address everywhere it appears – a potentially error-prone process. With normalization, the changes show up everywhere when she updates her email address in the Students table. This keeps the data consistent throughout the database.

What is data denormalization?

Data denormalization intentionally introduces redundancy into a database to improve read performance, speed up queries, and reduce computational overhead. Unlike normalization, which structures data to eliminate duplication and maintain consistency, denormalization reduces the need for complex joins by storing related data in a single table or document.

Example of denormalized data

Remember the university database we made earlier? Here’s what a denormalized version of the data looks like:

Alice’s information appears alongside every course she’s enrolled in. Similarly, course details like CourseName and Credits are repeated for each student, making this form suitable for read-heavy operations like displaying course enrollments for a student in a dashboard.

Normalized vs. denormalized data

Here’s a concise breakdown of the differences between normalized and denormalized data:

How to normalize data

Let’s say you’re responsible for designing a database schema for a pizza delivery company. Your initial design may look like this:

Let’s review how to normalize this schema step by step by passing it through various normalization forms.

Database normalization forms

First normal form (1NF)

For a database to be in first normal form, every value in every column of each table should be atomic, i.e., each value should only represent one thing. If we amend our pizza delivery service schema to conform to 1NF, it will look like this:

Notice that we divided the PizzaDetails column into two separate columns: PizzaType and Size. This ensures that all columns store atomic values.

Second normal form (2NF)

A table is in second normal form if each non-key attribute depends on the primary key, eliminating partial dependencies. To ensure our pizza schema conforms to 2NF, we need to split the table into two to eliminate these.

Customers and Orders table:

Pizza Details table:

Third normal form (3NF)

A table is said to be in third normal form if it’s already in 2NF, and all non-key attributes depend only on the primary key, not on other non-key attributes. 3NF eliminates indirect relationships to ensure the table is well structured and avoids redundancy.

To convert our pizza schema to third normal form, we need to:

• • Move reusable data into separate tables.
  • Create a Pizza Types table for pizza information like type and size.

Customers and Orders table:

Customers table:

Order Details table:

Pizza Types table:

The 3NF oath is also a good summarization of the first three normal forms:

“Every non-key attribute must provide a fact about the key, the whole key, and nothing but the key, so help me Codd.”

How to denormalize data

Denormalizing data typically involves duplicating data across multiple tables or databases. This approach can be applied by:

1. Identifying performance bottlenecks

• • Analyze slow queries that involve multiple joins.
  • Identify tables frequently joined together in read-heavy operations.
  • Use query profiling tools to measure performance impact.

2. Adding redundant data

• • Duplicate frequently accessed columns from related tables into a single table.
  • Store computed values (e.g., total sales and last login) instead of calculating them on the fly.

3. Merging tables (pre-join data)

• • Combine normalized tables that are frequently joined to reduce lookup times.
  • Example: Instead of storing Customers and Order Details separately and joining them, store customer data directly in the Order Details table.

4. Creating aggregated tables

• • Store precomputed summaries or rollups to avoid expensive calculations.
  • Example: Instead of computing total sales per month dynamically, maintain a Monthly Sales table.

5. Using key-value or document stores

• • If you’re using a NoSQL database, embed related data within a single document rather than normalizing it into separate collections.

6. Denormalizing selectively

• • Balance between redundancy and maintainability.
  • Only denormalize when performance gains justify the potential complexity.

7. Implementing triggers or batch updates

• • Use database triggers or scheduled jobs to keep denormalized data updated when source data changes.

Example of denormalization

Before (normalized data)

Customers table:

Order Details table:

After (denormalized data)

Order Details table (includes customer data):

This eliminates the need for a join when retrieving customer information with their orders.

Use cases

Data normalization

Banking systems:

• • Used for storing customer accounts, transactions, and loan details to maintain accuracy.
  • Banking systems use normalization to avoid duplicating sensitive financial data and ensure that updates (e.g., changing customer addresses) propagate correctly.

Healthcare systems:

• • Used for storing patient data, medical records, and appointments in separate tables.
  • Healthcare systems rely on normalization to reduce data duplication and ensure accurate patient information, which is essential for maintaining HIPAA compliance.

E-commerce applications:

• • Used for storing products, orders, customers, and inventory.
  • E-commerce applications use normalization to ensure correct inventory tracking and accurate customer orders.

Data denormalization

Data warehousing:

• • Used in business intelligence systems for consolidating sales, marketing, and operations data.
  • Data warehouses use denormalization to enable faster report generation and dashboard rendering, as aggregated data reduces the need for runtime joins.

Social media platforms:

• • Used for storing user posts, likes, comments, and follower data.
  • Social media platforms use denormalization to improve feed generation performance by storing pre-aggregated or redundant data, reducing query complexity.

Content delivery systems (CDNs):

• • Used by streaming services to store video metadata and user viewing history.
  • CDNs rely on denormalization to ensure fast retrieval of recommendations and watch history during peak load times.

Gaming applications:

• • Used to store player profiles, achievements, and in-game events in simplified structures.
  • Gaming systems use denormalization for low-latency data retrieval to enhance real-time gameplay experiences.

Benefits and challenges 

Benefits of data normalization

• • Eliminates data redundancy: Normalization reduces data duplication, leading to efficient storage usage and minimizing inconsistencies.
  • Ensures data integrity and consistency: Since data is stored in a structured manner with relationships maintained through keys, updates and deletions are more consistent across the database.
  • Reduces update anomalies: With less redundant data, changes (such as updating an address) only need to be made in one place, reducing the risk of inconsistent updates.
  • Enhances data security: Sensitive information can be stored in separate tables with restricted access, improving security measures.
  • Improves maintainability: A well-structured database is easier to modify, extend, and scale over time, as changes are localized to specific tables rather than spread across redundant data.
  • Optimizes transactional performance: Normalized databases ensure efficient transaction handling for systems requiring frequent inserts, updates, and deletes (OLTP systems).
  • Enforces referential integrity: Foreign keys ensure relationships between tables are maintained correctly, preventing orphaned or invalid records.

Challenges of data normalization

• • Slow read queries: Normalized databases often require multiple joins to retrieve related data, slowing down performance, especially for read-heavy applications.
  • Increased query complexity: Retrieving data requires writing complex SQL queries with multiple joins.
  • Higher CPU and memory usage: Every join operation requires additional processing, which can strain database resources.
  • Harder to scale horizontally: Normalized databases work well in single-node environments but may not scale efficiently in distributed architectures.
  • Increased development and maintenance effort: Designing a well-normalized schema requires careful planning and expertise (especially for larger applications).
  • Reporting and analytics challenges: Normalized databases are not ideal for online analytical processing (OLAP) systems because aggregating data for reports typically requires expensive joins and computations.
  • Data integrity constraints: Enforcing foreign key constraints and normalization rules can sometimes slow down bulk insertions and updates.

Benefits of data denormalization

• • Improved read performance: Denormalization speeds up read queries by reducing the number of joins required to retrieve data, which is especially useful in read-heavy applications.
  • Reduced query complexity: Since data is pre-joined or duplicated, queries become simpler, making it easier for developers to write and maintain queries.
  • Faster reporting and aggregation: Denormalized structures are well suited for analytical and reporting tasks, where data needs to be accessed quickly in bulk.
  • Better caching performance: By storing redundant data in a single document or table, caches can more effectively serve complete records and reduce database load.
  • Lower join overhead: Denormalization minimizes CPU and memory usage by reducing the need for joins.
  • Scalability: Denormalized data is often better suited for distributed databases (e.g., NoSQL databases) that prioritize horizontal scaling and fast data retrieval.
  • Faster indexing: Since related data is stored together, indexes can be more effective, leading to quicker lookups.

Challenges of data denormalization

• • Increased data redundancy: Data is duplicated across multiple tables or documents, leading to higher storage requirements.
  • Data inconsistency risks: Because denormalization involves redundant data, it’s crucial to remember to apply changes across all copies consistently.
  • Higher update and delete overhead: Updating or deleting records requires modifying multiple instances of the same data.
  • Harder to maintain and scale: Schema changes require updating multiple locations, increasing maintenance effort.
  • More complex write operations: Writes become more expensive because updates must be applied to multiple places.
  • Increased storage costs: Storing redundant data leads to higher disk usage, which can be costly for large applications.
  • Harder to enforce constraints: Denormalization often reduces the use of foreign keys, making referential integrity harder to enforce.

Key takeaway and resources

Using data normalization or denormalization depends on the specific needs of your application. Normalization ensures data integrity, consistency, and efficient updates, making it ideal for transactional systems (OLTP) where minimizing redundancy is crucial. On the other hand, denormalization enhances query performance and read efficiency, making it well suited for analytical workloads (OLAP), reporting, and NoSQL databases. Ultimately, understanding your database’s usage patterns, scalability needs, and performance goals is the best way to determine the best approach for your system.

To learn more about data management, check out the resources below:

• • How to denormalize imported documents from RDBMS tables using SQL++
  • Write-back cache – Concepts
  • What is data management? – Concepts