I wish to change the default aggregation from SUM to SUM on Distinct ID Values.
This is the current behaviour
ID Amount
1 $10
1 $10
2 $20
3 $30
3 $30
Sum Total = $90
By default, I am getting a sum of $90. I wish to do the sum on distinct ids and get a value of $60. How would I modify the default Aggregation Behavior to achieve this result?
Design your data as a many-to-many relationship: create one table/view having one record per ID and the amount column from the data shown in your question (the main fact table), and one table/view having one record per record of your data as shown in your question, presumably having another column, as otherwise it would not make any sense to have the data as shown in your question). This will be the m2m dimension table. Then, create a bridge table/view having the id of the m2m dimension table and your ID column.
Then create the following AS objects: A measure group from the main fact table, a dimension on column ID of the same table (in case there is no other column making a dimension table meaningful, in that case, you would better have a separate dimension table having ID as the primary key). Create a dimension from the m2m dimension table, and a measure group having only the invisible measure "count" from the bridge table. Finally, on the "Dimension Usage" tab of Cube Designer, set the relationship between the m2m dimension and the main measure group to be many to many via the bridge measure group.
See http://technet.microsoft.com/en-us/library/ms170463.aspx for a tutorial on many-to-many relationships.
Related
I have a table products with some signs of participation in certain promotions.
The condition of promotion work like this:
Take any product from set 1 AND take any product from set 2
There is a big table of sales
and table about regions for sales of point
I need to make a calculated measure for promotion 1 as the unique number salepoint_id for points that fulfill the condition promo 1
I prepared a scheme in powerpivot
Added calculated fields
And measures
Result correct
but:
The sales table is large and the calculated field is not the best approach
It is assumed in the table to use a hierarchy (Year/Quarter/Month, Region/Territory) and therefore the use Allexcept becomes impossible
How to create a calculated measure that counts the number of unique sale points have complied with the condition of promotion based on the context of the PivotTable?
Download link example
I have 2 fact tables with a measure group each, Production and Production Orders. Production has production information at a lower granularity (at the component level) productionorders has information at a higher level (order level with header quantities etc.).
I have created a surrogate key link between the two tables on productionorderid. As soon as I add Prod ID (from productiondetailsdim) to the pivot table it blats out the actual qty (from prod order measure group) and I cannot combine the qty's from the two measure groups.
How can I design the correct relationship between the two? Please see my dim usage diagram. Production Details is the dim that links the two fact tables, at the moment DimProductionDetails is in a fact relationship with Production. I'm not sure what the relationship should be with Production Order (it is currently many to many).
Please see example data between the two tables:
I have to be able to duplicate this behaviour:
Do you want the full actual qty from prod order measure group to repeat next to each product? If so a many-to-many relationship is right. I suspect once I explain how that many-to-many works you will spot the problem.
When you slice full actual qty from prod order measure group by product from the Production Details dimension it does a runtime join between the two measure groups on the common dimensions. So for example, if for if order 245295 has a date of 1/1/2015 while the production details for order 245295 have dates of 1/8/2015 then the runtime join will lose rows for that order and actual qty will show as null. So compare all the dimensions used on both measure groups and ensure all rows for the same order have the same dimension keys for those common dimensions. If for example dates differ then create a named query in the DSV that selects just the dimension columns from the production fact table which match the order fact table. Then create a new measure group off that named query and use the new measure group as the intermediate measure group in your many to many dimension. (The current many to many cell in the dimension usage tab should say the name of the new measure group not the existing Production measure group.)
Edit: if you want the actual qty measure to only show when you are at the order level and be null at the product level then try the following. Change the many-to-many relationship to a regular relationship and in the dialog where you choose how the fact table joins to the dimension change the dimension attribute to ProductionOrder_SK (which is not the key of the dimension) and choose the corresponding column in the fact table. Then left click on the Production Order measure group and go to the Properties window and set IgnoreUnrelatedRelationships to false. That way slicing actual qty by work center or by an attribute that is below grain in the Production Details dimension will show as null.
I have a fact table that has 4 date columns CreatedDate, LoginDate, ActiveDate and EngagedDate. I have a dimension table called DimDate whose primary key can be used as foreign key for all the 4 date columns in fact table. So the model looks like this.
But the problem is, when I want to do sub-filtering for the measures based on the date column. For ex: Count all users who were created in the last month and are engaged in this month. This is not possible to do with this design, coz when I filter the measure with create date , I can’t further filter for a different time window for engaged date. Since all the connected to same dimension, they are not working independently.
However, If I create a separate date dimension table for each of the columns, and join them like this then it works.
But this looks very cumbersome when I have 20 different date columns in fact table in real world scenario, where I have to create 20 different dimensions and connect them one by one. Is there any other way I can achieve my scenario w/o creating multiple duplicated date dimensions?
This concept is called a role-playing dimension. You don't have to add the table to the DSV or the actual dimensions one time for each date. Instead add the date once, then go to the dimension usage tab. Click Add Cube Dimension, and then choose the date dim. Right-click and rename it. Then update the relationship to use the correct fields.
There's a good article on MSSQLTips.com that covers this topic.
In SSAS is there a way to have a dimension relate to a fact table based on two columns in the fact table?
We have two tables: Location (Dimension) and Sales (Fact). The Location dimension has one column: "state". The Sales table has three columns: "saleAmount", "customerState" and "billingState" (because our customer can be in California but wants us to bill a company or branch in New York).
In SQL, if we want to see all the sales in California, we write our SQL query as:
select sum(saleAmount) from Sales where customerState = 'California' or billingState = 'California'
Is there a way to accomplish this in SSDT when building my cube so that when I'm using Excel as an end user tool and I select the state attribute from the Location dimension and the saleAmount measure, the saleAmount will be based on customerState or billingState? (I do not want to have role playing dimensions here - where one Location dimension is based on customerState and another Location dimension is based on billingState. I want one dimension matching up with both columns at once.)
Not the way you're thinking, but you can achieve what you want by doing this:
Create a view on your fact table, that is a UNION of facts related to customerState and facts related to billingState. This means the view will only have 1 State column, and if a fact has different values for customerState and billingState, then it will have two rows in the view.
Use the view instead of your table to populate your measure group in the cube.
Link the measure group to the Location dimension on the single State column in the Fact view.
Builder beware, this results in duplicate counting of facts when rolling up states where a single fact is in two different states.
Can Dimension Table became a fact table as well? For instance, I have a Customer dimension table with standard attributes such as name, gender, etc.
I need to know how many customers were created today, last month, last year etc. using SSAS.
I could create faceless fact table with customer key and date key or I could use the same customer dimension table because it has both keys already.
Is it normal to use Customer Dimension table as both Fact & Dimension?
Thanks
Yes, you can use a dimension table as fact table as well. In your case, you would just have a single measure which would be the count - assuming there is one record per customer in this customer table. In case you would have more than one record per customer, e. g. as you use a complex slowly changing dimension logic, you would use a distinct count.
Given your example, it is sufficient to run the query directly against the Customer dimension. There is no need to create another table to do that, such as a fact table. In fact it would be a bad idea to do that because you would have to maintain it every day. It is simpler just to run the query on the fly as long as you have time attributes in the customer table itself. In a sense you are using a dimension as a fact but, after all, data is data and can be queried as need be.