What is fact less fact table? where you have used it in your project?

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  • Jul 28th, 2006

Factless table means only the key available in the Fact there is no mesures availalabl

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  • Sep 18th, 2006

A fact table which doesn't contains any facts  then called as fact less fact table.

generally when we need to merge two data marts one data mart will not have any facts and other one common fact of both usable.

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  • Oct 22nd, 2006

Factless Fact Table contains nothing but dimensional keys. It is used to support negative analysis report. For example a Store that did not sell a product for a given period.


  • Apr 25th, 2007

Fact less table means which doesn't have measures.
Used only to put relation between the elements of various dimensions.

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In Schema level we can say as when we consider these as Count of Occurances/even that does not involved or getting aggreaged at the fact table means,we call these fact(has no measure but with events/occurance) called as Fact less Fact table.

for eg. No :of Accident for a Months
           No : of Policy has been closed this months.

Generally we  using the factless fact when we want events that happen only at information level but not included in the calculations level.just an information about an event that happen over a period.


  • May 12th, 2008

According to Kimball

"Factless fact table are the preferred method of recording the events in the DW where there is no natural numeric measurement associated with the event. Factless fact tables also are used to gurantee coverage"


  • Dec 11th, 2008

There are two kinds of fact tables which don't have any facts at all.  They are called FACTLESS FACT TABLES.  As per Kimball's law, every M-M relationship is a fact table.  They may consists of nothing but keys. 

First type of factless fact table records an event i.e. Attendence of the student.  Many event tracking tables in the dimensional DWH turns out to be factless table. 

Second type of factless fact table is called coverage table.  Coverage tables are frequently needed in (Dimensional DWH) when the primary fact table is sparse.

A fact which has possibly NO measure but can be seen as count of occurences or events is called factless fact.
example: no. of accidents per month on roads.

If i elaborate more, no. of accidents in a month will give you a basic count, however, you will be not able to make a business intelligence out of it. Hence this can't be seen as measure. It's just a statistics. Now, if you say what is measure in this case? then, you need to track one query more "at what speed accidents took place?".Here, SPEED is a measurable fact ...e.g 70km/hr, 140km/hr ...etc. Based on speed factor you can have BI, and each event count of accidents is assigned to each speed measure. hence BI says, by controlling speed measure you can control accidents. The count of accidents is just a statistics/information.....you cannot control it directly.
 It is a handy information which helps you in your business reporting.


  • Aug 14th, 2010

We cannot do any measures on it.
Values are generally text in nature.
We maintain a status to track the event.

Ex-Visitors to the office.
List of people for the web click.

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  • Jan 20th, 2015

A factless fact table is fact table that does not contain fact. They contain only dimensional keys and it captures events that happen only at information level but not included in the calculations level.
It is just an information about an event that happen over a period.
A factless fact table captures the many-to-many relationships between dimensions, but contains no numeric or textual facts. They are often used to record events or coverage information.
Common examples of factless fact tables include:
1-Tracking student attendance or registration events
2-Tracking insurance-related accident events
3-Identifying building, facility, and equipment schedules for a hospital or university
Factless fact tables are used for tracking a process or collecting stats.
They are called so because, the fact table does not have aggregate numeric values or information.
There are two types of factless fact tables: those that describe events, and those that describe conditions. Both may play important roles in your dimensional models.
Factless fact tables for Events
The first type of factless fact table is a table that records an event. Many event-tracking tables in dimensional data warehouses turn out to be factless.Sometimes there seem to be no facts associated with an important business process. Events or activities occur that you wish to track, but you find no measurements. In situations like this, build a standard transaction-grained fact table that contains no facts.
For eg.

The above fact is used to capture the leave taken by an employee.Whenever an employee takes leave a record is created with the dimensions.Using the fact FACT_LEAVE we can answer many questions like
• Number of leaves taken by an employee
• The type of leave an employee takes
• Details of the employee who took leave
Factless fact tables for Conditions
Factless fact tables are also used to model conditions or other important relationships among dimensions. In these cases, there are no clear transactions or events.It is used to support negative analysis report. For example a Store that did not sell a product for a given period. To produce such report, you need to have a fact table to capture all the possible combinations. You can then figure out what is missing.
For eg, fact_promo gives the information about the products which have promotions but still did not sell

This fact answers the below questions:
• To find out products that have promotions.
• To find out products that have promotion that sell.
• The list of products that have promotion but did not sell.
This kind of factless fact table is used to track conditions, coverage or eligibility. In Kimball terminology, it is called a "coverage table."
We may have the question that why we cannot include these information in the actual fact table .The problem is that if we do so then the fact size will increase enormously .
Factless fact table is crucial in many complex business processes. By applying you can design a dimensional model that has no clear facts to produce more meaningful information for your business processes. Factless fact table itself can be used to generate the useful reports.

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