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Types of Dimensions ,Types of Facts and Features of Dimension and Fact table

Types of Dimensions

A dimension table consists of the attributes about the facts. Dimensions store the textual descriptions of the business. Without the dimensions, we cannot measure the facts. The different types of dimension tables are explained in detail below.
 Conformed Dimension:
 Conformed dimensions mean the exact same thing with every possible fact table to which they are joined.
Eg: The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts.
 Junk Dimension:
 A junk dimension is a collection of random transactional codes flags and/or text attributes that are unrelated to any particular dimension. The junk dimension is simply a structure that provides a convenient place to store the junk attributes.
 Eg: Assume that we have a gender dimension and marital status dimension. In the fact table we need to maintain two keys referring to these dimensions. Instead of that create a junk dimension which has all the combinations of gender and marital status (cross join gender and marital status table and create a junk table). Now we can maintain only one key in the fact table.
 Degenerated Dimension:
 A degenerate dimension is a dimension which is derived from the fact table and doesn't have its own dimension table.
 Eg: A transactional code in a fact table.
 Role-playing dimension:
 Dimensions which are often used for multiple purposes within the same database are called role-playing dimensions. For example, a date dimension can be used for “date of sale", as well as "date of delivery", or "date of hire".

Types of Facts

A fact table is the one which consists of the measurements, metrics or facts of business process. These measurable facts are used to know the business value and to forecast the future business. The different types of facts are explained in detail below.
 Additive:
 Additive facts are facts that can be summed up through all of the dimensions in the fact table. A sales fact is a good example for additive fact.
 Semi-Additive:
 Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
Eg: Daily balances fact can be summed up through the customers dimension but not through the time dimension.
 Non-Additive:
 Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.
Eg: Facts which have percentages, ratios calculated.
 Factless Fact Table:
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".
Eg: A fact table which has only product key and date key is a factless fact. There are no measures in this table. But still you can get the number products sold over a period of time.
A fact tables that contain aggregated facts are often called summary tables
Cumulative: This type of fact table describes what has happened over a period of time. For example, this fact table may describe the total sales by product by store by day. The facts for this type of fact tables are mostly additive facts. The first example presented here is a cumulative fact table.

Snapshot: This type of fact table describes the state of things in a particular instance of time, and usually includes more semi-additive and non-additive facts. The second example presented here is a snapshot fact table.

Dimension Table features
1. It provides the context /descriptive information for fact table measurements.
2. Provides entry points to data.
3. Structure of Dimension - Surrogate key , one or more other fields that compose the natural key (nk) and set of Attributes.
4. Size of Dimension Table is smaller than Fact Table.
5. In a schema more number of dimensions are presented than Fact Table.
6. Surrogate Key is used to prevent the primary key (pk) violation(store historical data).
7. Values of fields are in numeric and text representation.

Fact Table features
1. It provides measurement of an enterprise.
2. Measurement is the amount determined by observation.
3. Structure of Fact Table - foreign key (fk), Degenerated Dimension and Measurements.
4. Size of Fact Table is larger than Dimension Table.
5. In a schema less number of Fact Tables observed compared to Dimension Tables.
6. Compose of Degenerate Dimension fields act as Primary Key.

7. Values of the fields always in numeric or integer form.
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