I am trying to build a calculated measure in SSAS that incorporates a dimension parameter. I have two facts: Members & Orders and one Dimension: Date. Members represents all the unique members on my site. Orders are related to members by a fact key representing a unique user. Orders also contains a key representing the vendor for an order. Orders contains a key to the date dimension.
FactMember
- MemberFactKey
- MemberId
FactOrder
- FactOrderKey
- OrderId
- FactMemberKey
- DimVendorKey
- DimDateKey
DimDate
- DimDateKey
- FYYear
The calculated measure I am trying to build is the number of unique vendors a member has ordered from. The value of the calculation must of course change based on the date dimension.
Wouldn't the DISTINCTCOUNT function be the one to use here? Creating a distinct count of Vendors could then be used in this query and elsewhere.
WITH MEMBER [Test]
AS
DISTINCTCOUNT([Vendor].[Vendor].[Vendor])
I will say in advance that this may well be slow (Depending on data volume/distribution), so if this query will be a popular/big part of the design it may be worth considering a restructure.
I am confused, it would make more sense to make Members and Orders both separate dimensions and then reference them from a FACT table, say Fact.Sales. This would eliminate the need to even build a calculated member if you keyed your Members dimension on some sort of member_key.
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
Firstly gonna show you example. We've got a fact table with some id, which is not primary key. Also we have dimension with all ids from fact table and names for that. Our id from fact table is a measure with aggregation function max. Is it possible to create calculated member, which will show name from our dimension using id from fact table? I know that it could be solved using rn and that structure:
Dimension.Hierahchy.Level.Item (meadures.rn).name
But is it possible to solve this another way?
We need to get key for number from measure. Something like that
Dimension.Hierahchy.Level.&[value of measures.maxid]
In mdx you can easily extract a maximum key of a set of members.
MAX(
Dimension.Hierahchy.Level.MEMBERS,
Dimension.Hierahchy.CurrentMember.MEMBERKEY
)
(the above is total guesswork as your current question does not include any example of mdx that you have already tried)
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.
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.
I have a cube with a measure called FactSales, which has entries for each day.
I have three Dimensions, Date, Customer and CustomerType.
Each FactSales row is linked to a date and customer by foreign key. customer is linked to customer type by foreign key.
From this I am able to spit out all sales figure for each customer on each date which is great.
I have multiple types : typeA, typeB, typeC, typeD, typeE.
What I want though is to create two calculated members which have the values aggregated for each customer by typeA, and then by everyother type.
What I have at the moment is something like
Case
When IsEmpty( [Measures].[FactSales] ) or [Customer].[CustomerType].currentmember <> [Customer].[CustomerType].&[typeA]
Then null
ELSE ([Customer].[CustomerType].&[typeA], [Measures].[FactSales] )
END
but I think this is wrong, and i also can't use this same method to get the value of all the other types excluding the typeA. I also dont get the aggregation when i roll it up to a higher level.
Can anyone help? I may not have explained my self well enough so please let me know if you need more info.
Why you are trying to do that? It's not good to store any calculation that is not related to single fact, data in fact table row should correspond only to one system fact (order, job, etc), and with aggregation you will be able to get data based on any dimension.
Your current structure is well and you can easily get calculations by slicing the cube over customer type dimension.