May 11, 2024
3 minute read
Denormalization is a data modeling technique that involves duplicating data in multiple tables to improve query performance. It is often used in situations where data is frequently accessed and there is a need for fast response times. While denormalization can improve performance, it can also lead to data redundancy and consistency issues if not implemented correctly.
Why Denormalization?
There are several reasons why one might want to learn about denormalization. First, it can help to improve the performance of data-intensive applications. By duplicating data in multiple tables, queries can be executed more quickly because the database does not have to join multiple tables to retrieve the necessary data. Second, denormalization can make data more accessible to users. By storing data in multiple tables, users can more easily find the data they need without having to navigate through complex relationships.
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Find a path to becoming a Denormalization. Learn more at:
OpenCourser.com/topic/gu3aoe/denormalizatio
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Denormalization.
One of the most well-regarded books on database systems. Discusses database normalization forms in a deep and detailed way. is useful as a reference and has great depth on relational database systems.
An overview of many aspects of database management systems, with a focus on practical topics. Discusses the pros and cons of denormalization and various tradeoffs involved with it.
A guide to creating data models for enterprise applications. Although most of the book deals with normalized models, it does include various tips on when and how to denormalize.
A readable, practical guide to database design for the non-specialist. It offers an accessible overview of many database topics, including when and why to denormalize.
A guide to using Unified Modeling Language (UML) for data modeling. It discusses how to use UML to create conceptual and logical data models. The book also includes a chapter on denormalization.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/gu3aoe/denormalizatio
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