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Fact Table

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Fact Table - definitions

Fact Table : The primary table in dimensional modelling that contains key business measurements. The facts are viewed by various Dimensions. See also Enterprise fact.

[Category=Data Quality ]

Source: Larry English, External, 25-Jan-2009 13:22

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Fact Table - The central table in a star join schema, characterized by a composite key. The Fact Table contains two types of fields: The fields storing the foreign keys that connect each particular fact to the appropriate value in each dimension, and the fields storing the individual facts - such as number, amount, or price.

[Category=Data Governance ]

Source: The Data Governance Institute, 07 December 2009 08:56:24, External

Fact Table -  In a star schema, the central table which contains the individual facts being stored in the database.

There are two types of fields in a fact table: 1. The fields storing the foreign keys which connect each particular fact to the appropriate value in each dimension.

2. The fields storing the individual facts (or measures) - such as number, amount, or price.

The granularity of the fact table is one of the most significant design decisions in creating a data warehouse. The facts should be as detailed as possible to allow for the data to be viewed from the greatest number of perspectives.

[Category=Data Warehousing ]

Source: SDG Computing Inc., 08 May 2010 09:05:54, SDG Computing, now offline

Fact Table - A central table in a data warehouse schema that contains numerical measures and keys relating facts to dimension tables. Fact tables contain data that describes specific events within a business, such as bank transactions or product sales.

[Category=Information Management ]

Source:, 19 June 2010 09:49:50, External

Fact Table - Usually the primary table that contains measurements (mostly numeric data like grade, wages, etc.). Also see dimension table. Imagine looking at a spreadsheet. The row and column names would be the dimensions and the numeric data within would be the facts.

[Category=Data Warehousing ]

Source: Aexis Business Intelligence, 07 December 2010 13:15:12, External

Fact - A fact table in an OLAP database is essentially the subject of the analysis. The dimensions can be thought of as foreign keys to this table or attributes of the fact. For instance, as described above, you might have sales as your fact table or subject. Attributes of a sale would be things like date and time, customer, product, geographical location, sales clerk, etc. All these characteristics of the sale could be your dimension tables.

[Category=Business Intelligence ]

Source: Deanna Dicken, 07 November 2012 09:47:00, External

fact table - A fact table is the central table in a star schema of a data warehouse. A fact table stores quantitative information for analysis and is often denormalized.

A fact table works with dimension tables. A fact table holds the data to be analyzed, and a dimension table stores data about the ways in which the data in the fact table can be analyzed. Thus, the fact table consists of two types of columns. The foreign keys column allows joins with dimension tables, and the measures columns contain the data that is being analyzed.

Suppose that a company sells products to customers. Every sale is a fact that happens, and the fact table is used to record these facts. For example:

Time ID Product ID Customer ID Unit Sold
4 17 2 1
8 21 3 2
8 4 1 1

Now we can add a dimension table about customers:

Customer ID Name Gender Income Education Region
1 Brian Edge M 2 3 4
2 Fred Smith M 3 5 1
3 Sally Jones F 1 7 3

In this example, the customer ID column in the fact table is the foreign key that joins with the dimension table. By following the links, you can see that row 2 of the fact table records the fact that customer 3, Sally Jones, bought two items on day 8. The company would also have a product table and a time table to determine what Sally bought and exactly when.

When building fact tables, there are physical and data limits. The ultimate size of the object as well as access paths should be considered. Adding indexes can help with both. However, from a logical design perspective, there should be no restrictions. Tables should be built based on current and future requirements, ensuring that there is as much flexibility as possible built into the design to allow for future enhancements without having to rebuild the data.

Related glossary terms: HP e3000, JAR file (Java ARchive), Create, Read, Update, Delete (CRUD) Cycle, OLAP cube, dimension table, customer data integration (CDI)

[Category=Data Management ]

Source:, 09 August 2013 09:33:03, External


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