Is Denormalization Right for Your Database Design?

  • Jeffrey Agadumo
  • June 5, 2023

If you’ve gone through our normalization piece, then you know how to clean up your database schema to avoid duplicate data and maintain data accuracy.

Normalizing data can sometimes lead to complicated queries, slower performance, and frequent read-intensive database interactions, especially when dealing with large amounts of data.

And that’s where denormalization comes into play; its seemingly unconventional technique involves intentionally introducing redundancy into a database to improve the accessibility of your data, among other things.

In this article, we delve into the concept of denormalization and provide a detailed comparison with normalization. We also offer a detailed list of denormalization methods that can improve the performance of your database queries, and we also talk about the best situations to use denormalization.

So stick around till the end; you might pick up a thing or two! 😉

The Concept of Denormalization

Denormalization techniques involve intentionally adding redundant data to a database and duplicating information from one table to another to eliminate the need for joins and improve the performance of read-heavy applications since it reduces the number of queries required to retrieve data.

In a normalized database, read-intensive queries, such as joins and others,  occur more frequently depending on the use case. As the size of the data grows, these types of queries may become slower, impacting the overall performance of the database. In such situations where scaling database resources is not feasible, denormalization can be a potential solution.

The techniques for denormalizing a database typically include the following:

1. Duplicate Columns

One of the most common ways of denormalizing a database is by duplicating columns in one or more database tables. In some cases, certain parts of the data are replicated across multiple databases when dealing with enterprise data. It can improve the performance of the database by reducing the need for cross-database joins.

2. Materialized Views

A materialized view is a pre-computed table that stores the result of a complex SQL query as a physical table in a database. It is a helpful tool for frequently used queries, as it reduces the processing needed to retrieve data and enhances the performance of SQL queries. By storing the results of complex queries in a materialized view, the data can be accessed more quickly, saving time and resources.

3. Partitioning

Partitioning involves dividing a large table into smaller, more manageable pieces based on specific criteria such as range or hash. It can improve query performance by allowing the database to retrieve only the partitions needed for a particular query. Partitioning can be vertical (column) or horizontal (row).

4. Summary Tables

Summary tables are tables that store aggregated data such as counts or averages. You can use them to speed up queries that require complex calculations or aggregations.

5. Cache Tables:

Cache tables are tables that store frequently accessed data in memory. It can improve query performance by reducing the number of disk reads required to retrieve data. It is caching frequently accessed data in memory or a local cache. It can improve query performance by reducing the database queries required to retrieve data.

Denormalization can boost database query performance, but it has its downsides. For instance, duplicating data across tables can make maintenance harder and lead to inconsistencies if updates are not synchronized. Denormalization can also raise storage requirements and sometimes not improve performance much.

Denormalization vs. Normalization

Normalization and denormalization are both techniques used in database design to improve data storage and retrieval efficiency and effectiveness.

In a normalized database, data is divided into as many separate tables as possible to eliminate data redundancy while maintaining optimal relationships between them, allowing for efficient storage and modification of data. Still, it can require multiple joins to retrieve information from different tables, reducing the performance of read requests.

On the other hand, denormalization aims to remedy this problem by doing the opposite, often intentionally adding redundant data to a database to improve query performance by reducing the need for joins between tables. It also involves adding calculated fields or aggregating data to avoid complex queries.

To better understand the differences between normalization and denormalization, let’s compare them by using some data modeling criteria:

Data Integrity:

Normalization aims to maintain data integrity by eliminating data redundancy and preventing inconsistencies, ensuring that each piece of data is only stored once and that any changes to that data should be in one place.

In this case, denormalization involves introducing redundancy into the database schema, which can increase the risk of data inconsistencies.

Query Performance:

In this case, normalization can sometimes result in slower query performance. You would often require multiple joins between tables and use complex queries to retrieve specific data, increasing the query execution time.

Denormalization, however, aims to improve query performance by reducing the need for joins between tables, making queries faster and more efficient, especially for complex queries involving multiple tables.

Data Redundancy:

Normalization reduces data redundancy by breaking down data into smaller, more manageable parts and storing each piece of data only once.

Denormalization is a technique used in database design that involves intentionally adding redundant data to improve query performance.


Normalization can make it easier to maintain a database because data changes only need to be made in one place. It also makes adding new data to the database easier because it already has a table structure.

In contrast, denormalization can make it more challenging to maintain a database because you may need to make data changes in multiple places, and the redundant data may need to be updated or deleted separately.

Data Model Complexity:

Normalization can lead to a more complex data model because the data is split into smaller tables with relationships between them, making it more challenging to understand and maintain the data model.

Denormalization can simplify the data model by reducing the number of tables and relationships, but it can also make the model less flexible and harder to maintain.

The Data Modeling Sweet Spot

While normalization and denormalization may sit on opposite sides of the data modeling spectrum, an optimal database schema aims to find the perfect balance between normalizing tables for better storage and denormalizing them for improved query performance.

Therefore the perfect schema should consider the specific needs and requirements of a given database and its users, keeping in mind the tradeoffs between both techniques of schema modeling.

In other words, modeling a database schema should not be reduced to a binary decision between normalization and denormalization. Instead, it should be tailored to your organization’s specific needs, considering the appropriate level of normalization for your schema while also allowing for necessary redundancy in data.

Some Scenarios For Denormalization

Denormalization can be beneficial in specific cases where query performance is essential due to frequent data retrieval. Here are some scenarios where denormalization can be helpful:

  • Reporting and Analytics: In reporting and analytics scenarios where you will need to make several read-intensive queries, denormalization can help to improve query performance by reducing the number of joins, further reducing their complexity.
  • High-Transaction Systems: Denormalization can help to improve query performance in high-transaction systems by reducing the number of database accesses required to retrieve data. It can be especially useful in systems where the response time is critical, such as financial systems or online stores.
  • Distributed Systems: Denormalization techniques are helpful in distributed systems as they reduce the system’s complexity by reducing the number of network calls required to retrieve data. They can be instrumental in systems with high network latency, such as systems that span multiple data centers or regions.
  • Data Warehousing: In data warehousing scenarios, denormalization can help to simplify the data model and improve query performance. It can significantly reduce the complexity of queries that involve joining multiple tables and performing aggregations.

Luckily, modern data warehousing solutions such as Snowflake possess advanced tools and features that facilitate data organization, creating a schema that aligns precisely with your unique specifications and needs.

The Ideal Data Modelling Assistant

Datameer is a data preparation and analytics platform with several features to facilitate data denormalization, making it easier to analyze data and speed up query performance. With Datameer, you can quickly transform complex data to create a more straightforward and easy-to-understand schema.

Datameer offers several tools that can help with both the normalization and denormalization of Snowflake data, such as:

Joining and Merging Data: Datameer allows users to join and merge multiple tables or datasets into a single, denormalized dataset. It can simplify analysis and improve performance by reducing the complexity of queries.

Data Transformation and Aggregation: Datameer provides a wide range of transformation and aggregation functions that you can use to simplify data and reduce redundancy. For example, users can group data by a specific attribute and calculate summary statistics such as counts, averages, or sums.

Pivot tables and charts: Datameer’s pivot tables, charts, and graphs allow users to quickly summarize and analyze large datasets. You can also use pivot tables to denormalize data by combining related tables into one table.

Some other features of Datameer that make it a breeze to use are:

Scalability: Datameer is designed to handle large volumes of data and can scale up or down to meet changing data processing needs.

Ease of use: Datameer’s user-friendly interface and drag-and-drop (no-code) functionality make it easy for users to prepare and analyze data without requiring extensive technical expertise.

Collaboration: Datameer supports collaboration between team members, allowing multiple users to work on the same data and share insights and results. 

Overall, Datameer is a powerful and flexible platform that provides users with a wide range of benefits, seamlessly integrating with Snowflake, making it an ideal solution for data preparation and analytics.


If you’re considering a data tool that provides limitless features and third-party integrations, then you should book a meeting and try Datameer today.