Denormalization

As explained in Effective Data Analysis

Denormalization is the deliberate combination or duplication of related data to reduce separation between tables and simplify analytical access.

Denormalization deliberately combines related data into cohesive analytical tables. Unlike normalized source structures designed to minimize duplication, denormalized tables reduce joins and calculations, making frequent analyses easier to read and interpret. section 11.4.2

Effective denormalization depends on:

• Selecting sources according to recurring queries and the information most valuable for analysis. section 11.4.2
• Organizing resulting tables into a star schema, with a central fact table connected to dimension tables. section 11.4.2
• Using facts such as sales transactions or page views alongside attributes such as customers or users. section 11.4.2

Figure 11.21 Star schemas have fact tables at the center connected to dimensions with related attributes.

A star-schema diagram shows how a central fact table connects to dimensions containing related attributes. section 11.4.2

How Effective Data Analysis covers it

How deep it goes

Defines denormalization and sketches star schemas, fact tables, and dimensions, but stops short of detailed implementation.

Examples

Uses sales transactions and page views as facts, with customer and user attributes as dimensions.

What you can do after

Apply it by selecting sources to combine according to recurring queries and valuable derived information.

How it connects

Connects denormalized data to normalization, star schemas, fact tables, dimension tables, and analytical queries.

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Also explained in

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  ScyllaDB in ActionDenormalization is a data-modeling technique that stores related data together, often duplicating information to support access patterns without additional lookups.
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  Microsoft Azure in ActionDenormalization is the intentional introduction of redundant or combined data into a relational schema to improve read performance or simplify access.
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  Grokking Relational Database DesignDenormalization is the deliberate introduction of controlled redundancy or less-normalized database structures to improve performance or meet operational needs.
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  MongoDB 8.0 in Action, Third EditionA denormalized data model stores related data together or redundantly to reduce the need for joins, separate queries, or cross-record operations.
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  Building Web APIs with ASP.NET CoreDenormalization is the deliberate combination or duplication of related data to simplify retrieval or improve access performance.

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Related concepts

• normalizationcontrasts with · Normalization is the process of transforming a value, representation, or data structure to satisfy a specified standard, scale, or structural constraint.
• database normalizationcontrasts with · Database normalization is a relational database design technique that organizes data into related tables to reduce redundancy and prevent update or dependency anomalies.
• joinaddresses · A join is an operation that combines records or rows from multiple data sources according to specified matching conditions or relationships.
• redundancyproduces · Redundancy is the presence of duplicate or overlapping information, components, or resources, whether intentional or unnecessary.
• optimizationkind of · Optimization is the systematic selection or adjustment of values, actions, or system designs to improve a specified objective, often subject to constraints.

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.