Dimension Table is a key component for Start Schema.
A dimension table contains the attributes which represent dimensions, along which the measurement is taken in fact table.
Every Dimension table must have a primary key that uniquely identifies each record of the table. It is commonly observed that the dimension table contain many attributes. Hence, it appears to be wide i.e. when you create a dimension table you will find it spreading horizontally.
The values of the attributes in dimension table are rarely numeric, most of the times you will find the values in attributes are in textual format. For example product name, brand, category, sub-category, etc.
Frequently you can observe, the attributes you come across in a dimension table are not directly related. Like, Product_brand has to do nothing with the package_date but still both could be the attributes of Product dimension table.
The dimension table is not supposed to be Normalized. This is because normalizing a table would create many intermediate tables. When a query picks up an attribute from dimension table and recovers measurements along that for the fact table, the query has to go through those intermediate tables which become inefficient. Hence, dimension tables are not Normalized.
For example, the time dimension would contain every hour, day, week, month, quarter and year that has occurred since you started your business operations. Product dimension could contain a name and description of products you sell, their unit price, color, weight and other attributes as applicable. Attributes would be a customer?s first and last name, age, gender etc.
In data warehousing, a conformed dimension is a dimension that has the same meaning to every fact with which it relates. The date dimension is an example of a shared dimension.
An outrigger is a dimension table or entity that is joined to other dimension tables in a star schema. Outriggers are used when a dimension table is snowflaked. Outriggers are tables or entities that are shared by more than one dimension.
A shrunken dimension is a subset of a dimension's attributes that apply to a higher level of summary.
For example, a Month dimension would be a shrunken dimension of the Date dimension.
Dimensions may have references to other dimensions. However, these relationships can be modeled with outrigger dimensions.
A table with multiple valid relationships between itself and another table is known as a role-playing dimension. This is most commonly seen in dimensions such as Time and Customer.
For example, the Sales fact has multiple relationships to the Time query subject on the keys Order Day, Ship Day, and Close Day.
A Junk Dimension is a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators.
Sometimes you may come across some dimensions in fact table, which are not additive at all. For example order_number, customer_id, you can not add these types of dimensions. However, in case, you need to find order made by a particular customer in this month; then you will need the customer_id to relate your search. These types if attributes or dimensions of fact table are called Degenerated Dimension.
This kind of dimension does not have its dimension as it is derived from the fact table.
They are used when the same fact table is paired with different versions of the same dimension.
Sequential processes, like web page events, mostly have a separate row in a fact table for every step in a process. It tells where the specific step should be used in the overall session.
A Fact table is a table that contains measurements along the attributes of dimension tables. It can contain the information at lowest possible level. Some fact table just contains summary data, called as Aggregated Fact Table. The fact table almost contains the date stamped data.
Fact table contains Concatenated key which is the concatenation of primary keys of all the dimension tables. The concatenated key of fact table must uniquely identify the row in a fact table.
Fully additive measures are those that can be easily summed up for all dimensions in fact table.
For example quantity_ordered, is an attribute that can be summed up for all dimensions. Like, we can take out total quantity_order, for a particular customer, region, date, brand, etc.
Semi-additive measures are those which can be summed along some dimensions of fact table but not all dimensions. Like, balance amount can not be summed up over time dimension as it changes over the time.
You can aggregate department headcounts to give an organization total, but you cannot aggregate them over time, so the Sales department headcount for March 31 may be 20 employees, and for April 30 the headcount may be 23, but that does not mean that the total headcount at the end of April is 43.
Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.
Profit margins are non-additive. If a department has two employees, and one employee has sold an item with a 55% profit margin and the other has sold an item with a 45% profit margin, the profit margin for the department is not 100%.
See also ETL Process
Admin on Oct 13, 2019 at 12:10 am
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