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Which is correct about the granularity of the fact table?
The granularity is the lowest level of information stored in the fact table. The depth of data level is known as granularity. In date dimension the level could be year, month, quarter, period, week, day of granularity.
What is the significance of the granularity of fact table?
By granularity, we mean the lowest level of information that will be stored in the fact table. This constitutes two steps: Determine which dimensions will be included. Determine where along the hierarchy of each dimension the information will be kept.
What is granularity of a table?
In dimensional modeling, granularity refers to the level of detail stored in a table. For example, a dimension such as Date (with Year and Quarter hierarchies) has a granularity at the quarter level but does not have information for individual days or months.
How do you calculate granularity?
After you gather all the relevant information about the subject area, the next step in the design process is to determine the granularity of the fact table. To do this you must decide what an individual low-level record in the fact table should contain.
Which is true in factless fact table?
A factless fact table is fact table that does not contain fact. They contain only dimesional keys and it captures 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.
How to determine the granularity of a fact table?
The first step in designing a fact table is to determine the granularity of the fact table. By granularity, we mean the lowest level of information that will be stored in the fact table. This constitutes two steps: Determine which dimensions will be included.
Why is it important to identify the grain in a fact table?
The grain conveys the level of detail that is associated with the fact table measurements. When you identify the grain, you also decide on the level of detail you want to make available in the dimensional model. If more detail is included, the level of granularity is lower.
Which is an example of granularity in dimensional modeling?
In dimensional modeling, granularity refers to the level of detail stored in a table. For example, a dimension such as Date (with Year and Quarter hierarchies) has a granularity at the quarter level but does not have information for individual days or months.
How to handle fact tables with different granularities in Dax?
Import the fact table without defining a relationship in the data model, and simulate the relationship (at the product category level) using a DAX expression that applies a corresponding filter at query time