Dimension Relationships | Microsoft Docs
Since the Driver dimension is common to both fact tables, you can use the Driver dimension to relate measures from both of them. Adding Many-To-Many Dimension and Fact Tables to Data Mart SSAS Dimension Relationship. Before we can setup the many-to-many relationship between Medical Claims and Diagnosis, we need to add the Diagnosis. SQL Server Analysis Services no When you define a dimension based on such a fact table item, the dimension is called a fact dimension. table. You then define the relationship between this new cube dimension and the.
A Measure is single numeric value whereas a Measure Group is a collection of measures.
What are the different types of Measures? Explain each one of them with an example. These are facts which can be added across all the associated dimensions. For example, sales amount is a fact which can be summed across different dimensions like customer, geography, date, product, and so on.
These are facts which can be added across only few dimensions rather than all dimensions. For example, bank balance is a fact which can be summed across the customer dimension i. However, the same fact cannot be added across the date dimension i. These are facts which cannot be added across any of the dimensions in the cube. For example, profit margin is a fact which cannot be added across any of the dimensions.
We cannot add profit margins across product dimensions. Derived facts are the facts which are calculated from one or more base facts, often by applying additional criteria. Often these are not stored in the cube and are calculated on the fly at the time of accessing them.
For example, profit margin. A factless fact table is one which only has references Foreign Keys to the dimensions and it does not contain any measures. These types of fact tables are often used to capture events valid transactions without a net change in a measure value. For example, a balance enquiry at an automated teller machine ATM. Though there is no change in the account balance, this transaction is still important for analysis purposes.
SSAS Interview Questions on Measures, Actions, and Storage
Textual facts refer to the textual data present in the fact table, which is not measurable non-additivebut is important for analysis purposes.
For example, codes i. What is the purpose of Dimension Usage settings? Explain different types of relationships between Facts and Dimensions.
A Cube Dimension is an instance of a database Dimension as explained in the previous tip. Following are the four different types of relationships between a Cube Dimension and a Measure Group: In a Regular relationship, primary key column of a dimension is directly connected to the fact table.
This type of relationship is similar to the relationship between a dimension and a fact in a Star Schemaand it can be based on either the physical primary key-foreign key relationship in the underlying relational database or the logical primary key-foreign key relationship defined in the Data Source View.
In a Referenced relationship, primary key columns of a dimension is indirectly connected to the fact table through a key column in the intermediate dimension table. This type of relationship is similar to the indirect relationship between a dimension and a fact, through an intermediate dimension, in a Snowflake Schema.
In a Fact relationship, the dimension table and the fact table are one and the same. In a Many-to-Many relationship, a dimension is indirectly connected to a Measure Group through an intermediate fact table which joins with the dimension table.
Using Many-to-Many Relationships in Multidimensional SQL Server Analysis Services
It is analogous to a scenario, where one project can have multiple project managers and one project manager can manage multiple projects. What are Calculated Members? How do they differ from Measures? The value of a measure base measure is stored in a cube as part of the cube processing process. What are Named Sets? What are the two types of Named Sets? A Named Set is a set of dimension members usually a subset of dimension members and is defined using MDX a Multidimensional Expression.Power Query Power BI: Transform 2 Fact Tables to Star Schema Data Model (Invoice Data) EMT 1498
Often Named Sets are defined for improved usability by the end users and client applications. Apart from that, they can also be used for various calculations at the cube level. Some of the examples of Named Sets are top 50 customers, top 10 products, top 5 students, etc.
Named Sets are of two types: Static Named Sets, when defined in cube, are evaluated during cube processing process. Dynamic Named Sets are evaluated each time the query is invoked by the user. What are the different properties associated with a KPI? A KPI is a measure of an organization's performance in a pre-defined area of interest. KPIs are defined to align with the pre-defined organizational goals and help the business decision makers gain insights into their business performance. Often KPIs have the following five commonly used properties: Indicates the name of the Key Performance Indicator.
Indicates the actual value of a measure pre-defined to align with organizational goals. Indicates the target value i. It is a numeric value and indicates the status of the KPI like performance is better than expected, performance is as expected, performance is not as expected, performance is much lower than expected, etc.
It is a numeric value and indicates the KPIs trend like performance is constant over a period of time, performance is improving over a period of time, performance is degrading over a period of time, etc.
Apart from the above listed properties, most of the times, KPIs contain the following two optional properties: If the cube contained a second measure group named Reseller Sales, you would be unable to dimension the facts in the Reseller Sales measure group by Geography because no relationship would exist between Reseller Sales and Geography.
There is no limit to the number of reference dimensions that can be chained together, as shown in the following illustration.
For more information about referenced relationships, see Define a Referenced Relationship and Referenced Relationship Properties. Fact Dimension Relationships Fact dimensions, frequently referred to as degenerate dimensions, are standard dimensions that are constructed from attribute columns in fact tables instead of from attribute columns in dimension tables.
Useful dimensional data is sometimes stored in a fact table to reduce duplication. The table contains attribute information not only for each line of an order issued by a reseller, but about the order itself.
Best Way for work with Multiple Fact Tables - Microsoft Power BI Community
The attributes circled in the previous diagram identify the information in the FactResellerSales table that could be used as attributes in a dimension. In this case, two additional pieces of information, the carrier tracking number and the purchase order number issued by the reseller, are represented by the CarrierTrackingNumber and CustomerPONumber attribute columns.
This information is interesting-for example, users would definitely be interested in seeing aggregated information, such as the total product cost, for all the orders being shipped under a single tracking number. But, without a dimension data for these two attributes cannot be organized or aggregated.
Using Many-to-Many Relationships in Multidimensional SQL Server Analysis Services
In theory, you could create a dimension table that uses the same key information as the FactResellerSales table and move the other two attribute columns, CarrierTrackingNumber and CustomerPONumber, to that dimension table.
However, you would be duplicating a significant portion of data and adding unnecessary complexity to the data warehouse to represent just two attributes as a separate dimension. Note Fact dimensions are frequently used to support drillthrough actions. Note Fact dimensions must be incrementally updated after every update to the measure group that is referenced by the fact relationship.
If the fact dimension is a ROLAP dimension, the Analysis Services processing engine drops any caches and incrementally processes the measure group. Many to Many Dimension Relationships In most dimensions, each fact joins to one and only one dimension member, and a single dimension member can be associated with multiple facts. In relational database terminology, this is referred to as a one-to-many relationship. However, it is frequently useful to join a single fact to multiple dimension members.
For example, a bank customer might have multiple accounts checking, saving, credit card, and investment accountsand an account can also have joint or multiple owners. The Customer dimension constructed from such relationships would then have multiple members that relate to a single account transaction.