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Denormalization is the process of combining data from multiple normalized tables into fewer larger tables in a database, to improve data retrieval performance. This technique is commonly employed in data warehousing, business intelligence, and big data applications where the focus is on analytics and reporting. Optimizing data for read-heavy operations, denormalization reduces the number of joins required for querying, consequently improving query performance.
Denormalization is characterized by its ability to:
In a denormalized database architecture, tables are restructured and merged to create a less complex schema, reducing the overall number of tables. The structure aims to minimize the number of joins, providing a more efficient data retrieval process for analytical queries.
Denormalization offers several advantages, primarily:
Denormalization use cases include:
Despite its benefits, denormalization comes with certain challenges and limitations:
Denormalization is the opposite of normalization, a process that organizes data into separate tables to reduce redundancy and improve data integrity. While normalization is ideal for transactional systems like OLTP databases, denormalization is more suitable for analytical systems like OLAP databases.
In a data lakehouse environment, denormalization can play an essential role in optimizing the data structure for analytics. By reducing the need for complex joins, denormalization can improve analytical query performance, ensuring efficient data processing and consumption by users. However, the lakehouse architecture's ability to handle complex data structures can help minimize the need for denormalization, by maintaining data in its raw format and allowing for efficient querying through optimized query engines like Dremio.
Denormalization may increase the risk of unauthorized data access, as sensitive data may be duplicated and spread across multiple tables. Securing access to these tables and maintaining consistent data security policies across the denormalized database are crucial considerations.
Denormalization focuses on improving query performance, especially in read-heavy use cases. By reducing the number of joins required for data retrieval, query execution times can be minimized. However, denormalization may negatively impact write performance due to increased data redundancy and storage requirements.