- What is factless fact table?
- What are conformed dimensions?
- What is star schema and snowflake?
- Can you join two fact tables?
- When would you use a junk dimension?
- What is a role playing dimension?
- What are the different types of dimensions?
- What is the difference between conformed dimension and role playing dimension?
- What is the difference between a dimension and a measure?
- What is difference between fact and dimension table?
- What is rapidly changing dimension?
- What is a junk dimension explain with example?
- What is difference between star and snowflake schema?
- Is fact table normalized or denormalized?
- What are the two types of dimensions?
- What are facts dimensions and measures?
- How do you make a junk dimension?
- What is Dimension and types of dimension?
What is factless fact table?
A factless fact table is a fact table that does not have any measures.
It is essentially an intersection of dimensions (it contains nothing but dimensional keys).
For example, you can have a factless fact table to capture student attendance, creating a row each time a student attends a class..
What are conformed dimensions?
Conformed dimensions are dimensions that are shared by multiple stars. They are used to compare the measures from each star schema . The reuse of conformed dimensions is very common in order to “support true, cross-business process analysis” .
What is star schema and snowflake?
The star schema is the simplest type of Data Warehouse schema. It is known as star schema as its structure resembles a star. A Snowflake Schema is an extension of a Star Schema, and it adds additional dimensions. It is called snowflake because its diagram resembles a Snowflake.
Can you join two fact tables?
The answer for both is “Yes, you can”, but then also “No, you shouldn’t”. Joining fact tables is a big no-no for four main reasons: 1. Fact tables tend to have several keys (FK), and each join scenario will require the use of different keys.
When would you use a junk dimension?
Junk dimensions are used to reduce the number of dimensions in the dimensional model and reduce the number of columns in the fact table. A junk dimension combines two or more related low cardinality flags into a single dimension.
What is a role playing dimension?
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. Decide how to use these roles with other facts that do not share the same concepts. …
What are the different types of dimensions?
Types of DimensionsSlowly Changing Dimensions.Rapidly Changing Dimensions.Junk Dimensions.Stacked dimensions.Inferred Dimensions.Conformed Dimensions.Degenerate Dimensions.Role-Playing Dimensions.More items…
What is the difference between conformed dimension and role playing dimension?
Cube dimension is an instance of a database dimension in a cube is called a cube dimension and relates to one or more measure groups in the cube. A database dimension can be used multiple times in a cube. Those referenced and renamed for purpose database dimension is role-playing dimension.
What is the difference between a dimension and a measure?
What’s the difference between a measure and dimension? Measures are numerical values that mathematical functions work on. … Dimensions are qualitative and do not total a sum. For example, sales region, employee, location, or date are dimensions.
What is difference between fact and dimension table?
The fact table mainly consists of business facts and foreign keys that refer to primary keys in the dimension tables. A dimension table consists mainly of descriptive attributes that are textual fields. When comparing the size of the two tables, a fact table is bigger than a dimensional table.
What is rapidly changing dimension?
Rapidly changing dimensions are dimensions where the attribute values of the dimension change frequently causing the dimension grow rapidly if you a have designed the dimension to capture the changes as a Type 2 dimension.
What is a junk dimension explain with example?
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.
What is difference between star and snowflake schema?
Star and snowflake schemas are similar at heart: a central fact table surrounded by dimension tables. The difference is in the dimensions themselves. In a star schema each logical dimension is denormalized into one table, while in a snowflake, at least some of the dimensions are normalized.
Is fact table normalized or denormalized?
According to Kimball: Dimensional models combine normalized and denormalized table structures. The dimension tables of descriptive information are highly denormalized with detailed and hierarchical roll-up attributes in the same table. Meanwhile, the fact tables with performance metrics are typically normalized.
What are the two types of dimensions?
The basic types of dimensioning are linear, radial, angular, ordinate, and arc length.
What are facts dimensions and measures?
A fact is represented by a box that displays the fact name along with the measure names. Small circles represent the dimensions, which are linked to the fact by straight lines (see Figure 1). A dimensional attribute is a property, with a finite domain, of a dimension.
How do you make a junk dimension?
Design Tip #113 Creating, Using, and Maintaining Junk DimensionsBuild the Initial Junk Dimension. If the cardinality of each attribute is relatively low, and there are only a few attributes, then the easiest way to create the dimension is to cross-join the source system lookup tables. … Incorporate the Junk Dimension into the Fact Row Process. … Maintain the Junk Dimension.
What is Dimension and types of dimension?
Types of Dimensions are Conformed, Outrigger, Shrunken, Role-playing, Dimension to Dimension Table, Junk, Degenerate, Swappable and Step Dimensions. Five steps of Dimensional modeling are 1.