Data Denormalization: What It Is and Why It’s Useful

The total volume of data generated, stored, and consumed worldwide is expanding quickly and is expected to reach 394 zettabytes by 2028. This rapid growth demands better ways to manage and access data in many business operations, such as real-time analytics and large-scale reporting.

While normalized databases are excellent for keeping data organized, they can slow down processes requiring complex queries. In these scenarios, you can utilize the data model denormalization concept to enhance database query performance and facilitate faster data retrieval.

This article will help you learn how data denormalization works, explore its techniques, understand benefits, and discover use cases, as well as best practices.

What Is Data Denormalization?

Data model denormalization is an optimization process that allows you to introduce some pre-computed redundancy into a normalized database. While data normalization helps you reduce duplication and ensure data integrity by breaking data into smaller and related tables, denormalization sacrifices some of this integrity. However, a denormalized database is not the same as a database that has never been normalized. As a result, you can apply this denormalization approach after the database normalization process.

Let’s look at a few key features of this optimization technique:

• Enhanced Query Performance: Denormalization enables you to reduce complex join queries by consolidating data into a single table. This speeds up query execution in frequent and complex read operations.
• ‍Simplified Data Retrieval: You store data in fewer tables or precomputed formats through the denormalization approach. Consequently, you can retrieve the data faster for end-user applications.
• ‍Quick Aggregation: By storing aggregated values such as totals, averages, or counts directly within the database, denormalization allows you to minimize the runtime calculations. Such characteristics ensure rapid execution of analytical queries for reporting and real-time insights.

How Does Data Denormalization Work?

To understand the data denormalization model, it is essential first to learn how data is organized in a normalized database. Then, you can explore how to apply denormalization to improve performance when handling large datasets or complex queries.

In a normalized database, you can split the data into multiple related tables to reduce duplication and ensure data integrity. Here’s an example of how an e-commerce platform might store customer and order information in a normalized structure.

In this example, you can see that the Customer table contains customer information. The Orders table includes details about customer orders, with a foreign key CustomerID linking it to the Customers table.

However, there is a problem with this normalized structure. Consider the online platform needs to display all orders placed by a particular customer. To achieve this in a normalized database, the website would run a JOIN query to combine data from the Orders and Customers tables.

While this approach works, the problem arises as the database grows or when the system experiences a high volume of queries, requiring repeated join operations. In large databases with thousands or millions of orders and customers, performing these JOIN operations becomes computationally expensive, significantly slowing down website performance.

To resolve issues within the normalized databases, you can apply denormalization techniques in any of the following ways:

• Directly add the Name column from the Customers table to the normalized Orders table, eliminating a join query to retrieve orders for a specific customer.

• Create a new table that merges the data from both the Customers and Orders tables. This table would store each order along with the full customer information.

Five Techniques of Data Denormalization

To optimize database performance, especially for read-heavy operations or complex queries, you can leverage various techniques of data denormalization. Here are five key techniques commonly used to achieve this:

Pre-Joining Tables

A pre-joining table is a technique that you can utilize when join operations are computationally expensive. In this approach, you can duplicate specific data fields (columns) across multiple normalized tables to minimize frequent joins or lookups during queries.

For example, customer details might be copied into the orders table. As a result, you do not have to join the orders table with a separate customer table when querying orders.

This form of denormalization intentionally introduces redundancy into the tables for performance optimization.

Mirrored Tables

This technique involves creating a full or partial copy of existing tables. In a mirrored table approach, you can consolidate logically related data into a single table, even if it results in duplication. This enables you to simplify the schema by reducing the fragmentation of similar data between tables.

For instance, instead of separating a book’s information, subject, and publisher into different tables, you can store them together to make querying more efficient.

Grouping also reduces the join requirements and simplifies the data access.

Table Splitting

Table splitting refers to decomposing a large table into smaller, more manageable tables for faster query processing. You can perform table splitting in two ways:

Horizontal Table Splitting

In horizontal table splitting, you can distribute rows from a table across multiple tables while keeping columns the same. This technique is helpful when you can logically divide the data based on certain criteria, such as regions, departments, or time periods.

Consider a Student_Information table that holds data for all students in different departments of a university.

With horizontal splitting, you could create separate tables for each department. Each of these smaller tables will have the same columns but will only contain data specific to that department.

This table splitting method allows you to query data for a specific department quickly because the query only scans a smaller table.

Vertical Table Splitting

Vertical splitting involves dividing a table based on columns while applying the primary key to each partition. It is beneficial when certain data fields are queried more frequently than others, enabling optimized access to those columns.

Consider a Patient table in a hospital database:

In vertical table splitting, you can create two new tables—one for patient details and one for medical history.

Here, queries needing patient details can be accessed directly from the Patient_Details table. For a medical history, you can perform a separate query with the Patient_Medical_History table.

Adding Derived Columns

Adding derived columns is a technique that helps you enhance query performance by storing pre-calculated values within the table itself. A derived column is a new column that is created by performing a calculation on existing data. By adding a derived column, you can avoid recalculating the value to improve efficiency.

Let’s consider two tables:

Now, you may need to display the total marks for each student along with their details. Without a derived column, the database would need to sum the marks for each student’s assignments instantly using a JOIN query. If the database is large or the query needs to run frequently, using the JOIN operation can be inefficient.

To optimize this, you can add a Total_Marks derived column directly to the Student table.

Here, the Total_Marks column stores the sum of marks for each student from the Student_Grades table. You can calculate this value once and update it when new grades are added using the UPDATE statement. Once all the data is in the Students table, you can query it directly using the SELECT command without performing any join or aggregation operations.

Materialized Views

Materialized views are pre-computed query results stored as a separate table within the database. They are designed to optimize performance for expensive queries, such as those involving joins and aggregations. By storing the results of these queries, materialized views facilitate quick data retrieval from the database without executing the same query repeatedly.

Let’s see an example of materialized view:

Consider a Sales database with two tables—Orders and Products.

Suppose you want to calculate the total sales for each product by multiplying the quantity sold by the price and then summing it up for each product. The query will be:

SELECT p.Product_Name, SUM(o.Quantity * p.Price_in_Dollars) AS Total_Sales
FROM Orders o
JOIN Products p ON o.Product_ID = p.Product_ID
GROUP BY p.Product_Name;

This query uses a JOIN operation between the Orders and Products tables, which makes it expensive every time the query is executed. In such situations, you can create a materialized view to store the result of the above query; you do not have to recompute the query.

CREATE MATERIALIZED VIEW Product_Sales_View AS
SELECT p.Product_Name, SUM(o.Quantity * p.Price_in_Dollars) AS Total_Sales
FROM Orders o
JOIN Products p ON o.Product_ID = p.Product_ID
GROUP BY p.Product_Name;

Instead of running the complex join and aggregation query, you can easily query the materialized view Product_Sales_View to get the total sales for each product.

Normalized vs. Denormalized Data

This section entails a tabular comparison between normalized and denormalized data based on several aspects:

Let’s understand how each aspect differentiates both data types in detail:

Data Structure

In normalized data, the data is broken into different tables with unique relationships through foreign keys. This is to ensure that there is no unnecessary duplication of information. In denormalized data, on the other hand, related data is stored together, and redundancy is intentionally added to optimize the query speed.

Storage Efficiency

Normalized data is more storage-efficient because redundancy is minimized. It avoids repeating information in multiple places. In contrast, denormalized data uses a high amount of storage space as the same data might be duplicated across several records to make queries faster.

Query Optimization

In a normalized database, you must use numerous joins to fetch related data, which can slow down the query execution. Conversely, with a denormalized database, you can speed up queries since the linked data is already combined, reducing the need for complex joins.

Data Integrity

Normalized data ensures strong data integrity within the databases. This is due to the fact that each piece of information is stored in only one place. Changes are easier to manage, as they are only required to be made once. Denormalization, however, can lead to issues like inconsistent updates because the same data may appear in multiple places.

Write Performance

You can perform write operations like inserts, updates, or deletes with normalized data better as it avoids the need to modify multiple records. On the other hand, denormalized data requires more complex writes because changes must be made between various copies of the data.

Data Maintenance

The use of normalized data minimizes the risk of inconsistency and simplifies maintenance. In contrast, denormalized data can make it difficult to maintain consistency as you must apply updates across all instances where the data is stored.

GenAI Support

Data normalization for GenAI applications provides benefits, but it leads to a loss in fidelity–the specific details that may be omitted when breaking data into standardized tables. This can affect the results when those details are critical for specific analysis or personalized outputs. When denormalizing data, you can retain the contextual relationships and granular details within a single structure to simplify your GenAI workflows.

Use Case

Normalized data is commonly used in transactional systems where consistency and accuracy are essential. Denormalized data, on the other hand, is most beneficial in reporting or analytics platforms where speed is prioritized over consistency.

Benefits of Data Model Denormalization

• Reduce Query Complexity: With data model denormalization, you can simplify queries by minimizing the number of join operations required. These simpler queries allow you to significantly reduce the risk of bugs related to the database operations.
• ‍Enhance Application Scalability: Denormalization helps you cut down the number of database transactions required when reading data. By decreasing the transaction volumes, denormalization helps you improve the scalability of your application, allowing it to handle varying user loads more efficiently.
• ‍Generate Data Reports Faster: Your organization can generate reports such as usage statistics and sales reports, which require data aggregation and summarization from large datasets. Data denormalization techniques like mirrored tables enable you to optimize databases specifically for faster report generation without impacting the performance of master tables.

Practical Use Cases of Denormalized Data

Denormalized data plays a crucial role in various practical scenarios where performance and efficiency are important.

Retail Data Warehousing with BigQuery

In a retail business, consider a data warehouse storing transactional data. Conventionally, the fact table would be normalized with separate dimensional tables. Querying this normalized data warehouse would require multiple joins, increasing complexity and query latency in case of large datasets.

With BigQuery, you can denormalize data within a normalized warehouse by embedding the related dimensions into the fact table using its nested and repeated structures. By leveraging BigQuery’s data types like ARRAY and STRUCTs, you can store and query denormalized data efficiently without requiring table JOINs. This denormalization approach within BigQuery allows you to accelerate the query performance for modern retail analytics.

Salesforce Customer Management

Salesforce is a leader in customer relationship management (CRM). One of the core components of Salesforce is its data structure, which is built around objects. These objects help to organize and store crucial data on the platform.

Among these Salesforce objects, the Contact object, which serves as a central repository for key customer information, is denormalized. This object keeps multiple phone numbers, email addresses, and physical addresses within the same object, enabling an efficient and comprehensive view of each customer.

Data Model Denormalization Best Practices

When implementing data denormalization, follow these best practices for better database optimization:

• To perform denormalization, consider normalizing the database first for better data integrity and then denormalizing based on performance needs.
• Focus on denormalizing data structures that are primarily used in frequently accessed queries, reports, or dashboards.
• Analyze your database queries to identify the specific joins or retrieval processes causing slowdowns.
• Regularly monitor your denormalized structures to check they continue to meet your performance requirements.
• As your data grows, you may need to reevaluate and adjust your denormalized data to maintain optimal efficiency.
• Since denormalization introduces data redundancy, ensure you have reliable mechanisms in place to keep all redundant data synchronized whenever the original data changes.

How Airbyte Helps in Denormalizing Your Data?

Denormalization involves merging data across multiple tables into a single, consolidated table for improved query performance. Utilizing an efficient data integration and replication platform like Airbyte can help you streamline this process with the help of its 550+ pre-built connectors. With these connectors, you can quickly extract data from numerous sources and load it into a destination of your choice, such as BigQuery, Snowflake, or PostgreSQL. Further, you can integrate Airbyte with dbt, a robust data transformation tool, to perform denormalization using SQL queries.

Here are additional features of Airbyte:

• Custom Connector Development: Airbyte allows you to build a custom connector using no-code Connector Builder, low-code CDK, or language-specific CDKs. To accelerate the development process, you can leverage the AI Assistant feature within the no-code Connector Builder, which automatically prefills the required configuration fields.
• ‍Change Data Capture (CDC): Airbyte supports CDC, allowing you to track the changes from the source system and copy them to the destination system to maintain efficient data synchronization.
• ‍Developer-Friendly Pipeline: Airbyte’s open-source library, PyAirbyte, enables you to extract data from various sources using the Airbyte connectors. You can then ingest the data into SQL-like internal caches, including Snowflake, DuckDB, or BigQuery. These caches are compatible with Python libraries like Pandas and AI frameworks like LangChain. By converting internal caches to Pandas DataFrame, you can perform transformations to align data with the target schema and load it into the desired destination.

Summing It Up

Data model denormalization is a powerful approach for optimizing your database performance, especially in read workloads, real-time analytics, and GenAI applications. Consolidating data and reducing the need for expensive joins help you enable faster query execution, create simpler queries, and enhance scalability. However, these benefits have a few challenges, such as increased storage requirements and potential redundancy. Following the best practices highlighted in this comprehensive guide, you can implement data denormalization effectively.