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From Wikipedia

Fact table

In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is often located at the centre of a star schema or a snowflake schema, surrounded by dimension tables.

Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain. The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a SALES fact table might be stated as "Sales volume by Day by Product by Store". Each record in this fact table is therefore uniquely defined by a day, product and store. Other dimensions might be members of this fact table (such as location/region) but these add nothing to the uniqueness of the fact records. These "affiliate dimensions" allow for additional slices of the independent facts but generally provide insights at a higher level of aggregation (a region contains many stores).


If the business process is SALES, then the corresponding fact table will typically contain columns representing both raw facts and aggregations in rows such as:

  • $12,000, being "sales for New York store for 15-Jan-2005"
  • $34,000, being "sales for Los Angeles store for 15-Jan-2005"
  • $22,000, being "sales for New York store for 16-Jan-2005"
  • $50,000, being "sales for Los Angeles store for 16-Jan-2005"
  • $21,000, being "average daily sales for Los Angeles Store for Jan-2005"
  • $65,000, being "average daily sales for Los Angeles Store for Feb-2005"
  • $33,000, being "average daily sales for Los Angeles Store for year 2005"

"average monthly sales" is a measurement which is stored in the fact table. The fact table also contains foreign keys from the dimension tables, where time series (e.g. dates) and other dimensions (e.g. store location, salesperson, product) are stored.

All foreign keys between fact and dimension tables should be surrogate keys, not reused keys from operational data.

The centralized table in a star schema is called a fact table. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys. Fact tables contain the content of the data warehouse and store different types of measures like additive, non additive, and semi additive measures.

Measure types

  • Additive - Measures that can be added across all dimensions.
  • Non Additive - Measures that cannot be added across all dimensions.
  • Semi Additive - Measures that can be added across few dimensions and not with others.

A fact table might contain either detail level facts or facts that have been aggregated (fact tables that contain aggregated facts are often instead called summary tables).

Special care must be taken when handling ratios and percentage. One good design rule is to never store percentages or ratios in fact tables but only calculate these in the data access tool. Thus only store the numerator and denominator in the fact table, which then can be aggregated and the aggregated stored values can then be used for calculating the ratio or percentage in the data access tool.

In the real world, it is possible to have a fact table that contains no measures or facts. These tables are called "factless fact tables", or "junction tables".

The "Factless fact tables" can for example be used for modeling many-to-many relationships or capture events

Types of fact tables

There are basically three fundamental measurement events, which characterizes all fact tables.

  • Transactional
A transactional table is the most basic and fundamental. The grain associated with a transactional fact table is usually specified as "one row per line in a transaction", e.g., every line on a receipt. Typically a transactional fact table holds data of the most detailed level, causing it to have a great number of dimensions associated with it.
  • Periodic snapshots
The periodic snapshot, as the name implies, takes a "picture of the moment", where the moment could be any defined period of time, e.g. a performance summary of a salesman over the previous month. A periodic snapshot table is dependent on the transactional table, as it needs the detailed data held in the transactional fact table in order to deliver the chosen performance output.
  • Accumulating snapshots
This type of fact table is used to show the activity of a process that has a well defined beginning and end, e.g., the processing of an order. An order moves through specific steps until it is fully processed. As steps towards fulfilling the order are completed, the associated row in the fact table is updated. An accumulating snapshot table often has multiple date columns, each representing a milestone in the process. Therefore, it's important to have an entry in the associated date dimension that represents an unknown date, as many of the milestone dates are unknown at the time of the creation of the row.

Steps in designing fact table

  • Identify a business process for analysis (like sales).
  • Identify measures or facts (sales dollar), by asking questions like what ‘number of’ XX are relevant for the business process (Replace the XX, and test if the question makes sense business wise).
  • Identify dimensions for facts (product dimension, location dimension, time dimension, organization dimension), by asking questions which makes sense business wise, like 'Analyse by' XX, where XX are replaced with the subject to test.
  • List the columns that describe each dimension (region name, branch name, business unit name).
  • Determine the lowest level (granularity) of summary in a fact table (e.g. sales dollar).

Or utilize the four step design process described in Kimball

From Yahoo Answers

Question:+ | @ # & ----------------------------- @ | & @ # # | @ # && | # & @ Does this system have an identity element for addition? If so what? Use the table and the missing addend definition of subtraction to find the value of @ - # and how you arrived at your answer.

Answers:The # symbol is the identity element. Notice in either the row with # or the column with #, when anything is added to #, you get whatever it was you were adding to it. Based on this definition, we can see that if we have @ - # this will give you @ since you are subtracting zero from @.

Question:Where can i find the truth table of the 2bit full adder for addition and subtraction, and 2bit binary multiplier? Should there be 16 different combinations for each calculation ??

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Question:I'm in the 8th grade and I have a problem that is concerning me for the future. I haven't memorized, or know by heart, simple subtractions and addition problems. Is this a problem that I should worry about? I'm a straight A student and I want to improve my learning ability. Is there any ways I can fix this issue?

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From Youtube

Addition and Subtraction Fact Families :Educational video about addition and subtraction fact families.

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