What is Data Denormalization?
Data denormalization is the process of restructuring a relational database by adding redundant data to one or more tables. The aim is to improve query performance by reducing the need for joins. The term refers to removing one or more normalization forms to achieve this goal. Normalization is a database design technique focused on minimizing data redundancy and inconsistency.
The idea behind data denormalization is that it is faster to fetch data from a single table than to join data from multiple tables. When dealing with large datasets, the performance boost can be significant. By denormalizing data, a company can optimize query performance and, as a result, enable faster data processing and analytics.
How Does Data Denormalization Work?
Denormalization adds redundant data to one or more tables, reducing the need to perform additional joins to retrieve data. For example, instead of having a customer table with information about each customer and a separate order table with information about each order, a company may choose to add customer information to each order. This eliminates the need for a join between the two tables when querying a customer's orders.
Data denormalization is usually done in conjunction with a data warehouse or data lakehouse. Redundant data is added to the same physical table or partitioned into a different one, making it easier to query and analyze data over time. As compared to a normalized database, a denormalized database can have a lot of duplicates, which could occupy much storage space. However, this redundancy can trade storage space for processing speed in analysis.
Why is Data Denormalization Important?
Data denormalization helps organizations improve query performance, which speeds up data processing and analytics. When working with large datasets, query performance can be significantly improved by minimizing the need for joins. Reducing the number of joins required lowers the I/O and CPU demand in a system, which can lighten the operational workload of the system and increase its response rates. By optimizing query performance, users can access insights more quickly and make faster, more informed business decisions.
The Most Important Data Denormalization Use Cases
Data denormalization is not a one-size-fits-all solution, and there are many use cases where it is appropriate:
- Reporting applications with many tables that require high-performance, low-latency querying
- Applications with data models that are too complex for OLAP cube processing
- Real-time data analysis that requires low performance overhead on the system
Technologies or Terms that are Closely Related to Data Denormalization
Data denormalization is related to a number of other database concepts and technologies:
- Data Warehousing
- Data Lakehouse
- OLAP (Online Analytical Processing)
- Data Modeling
- ETL (Extract, Transform, Load)
- Data Cleansing
Why Dremio Users Would be Interested in Data Denormalization
Dremio provides advanced data processing and analytics capabilities, making it an ideal platform for data denormalization. By leveraging Dremio's data lakehouse architecture, users can efficiently store and query large amounts of denormalized data, resulting in faster and more accurate insights. Additionally, Dremio's advanced data modeling features enable users to easily create the denormalized data sets necessary for optimal query performance. Overall, data denormalization is a critical technique for any business that relies on data analytics, and Dremio is an ideal platform for implementing this optimization strategy.
