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.
Related
I'm very new to data warehousing and dimensional modelling. For a uni project I started out with a database that I need to turn into a data warehouse for analyses. To end up with a clean star schema, I had to denormalize a few tables into 1 fact table. The downside to this is the amount of redundancy.
Below is a part of the data from the fact table:
A voyage consists of multiple shipments, and a shipment can consist of multiple different items. In this example, containers 1-2000 of shipment 1 contain item 3, and containers 2001-5000 contain item 1. The total amount of containers for this shipment is 5000, obviously. However, for data analysis purposes, I need to calculate a measure for the total amount of containers per voyage. This presents a problem with the current fact table, because I have a record for each different item. So for voyage 1, the actual total amount should be 9200, but because of the duplication I'll end up with 19400, leading to an incorrect measure.
I need to find a way to get rid of the duplicates in my calculation, but I can't find a way to do so. Any help would be much appreciated.
What you'll need to do is group by your shipments (CTE, inner query, temp table, etc) to get the number of containers per shipment, then group by your voyages to get the number of containers per voyage.
Here's an example with an inner query:
SELECT voyage_id, SUM(num_ship_containers) AS num_voyage_containers
FROM (
SELECT voyage_id, shipment_id, MAX(container_end) AS num_ship_containers
FROM ShippingWarehouse
GROUP BY voyage_id, shipment_id
) AS ship_data
GROUP BY voyage_id;
voyage_id
num_voyage_containers
1
9200
Try it out!
I have two dimensions: location and date. There's one fact table (x) consisting of measures with respect to location and date. Now, I have a requirement for including target KPI measures for each of the 60 locations I have in location dimension table. So, each measure in the fact table (x) has a benchmark measure (KPI). I cannot add it in the fact table (x) because the KPI values would repeat all across the depth of the table.
How do I re-model the star schema to incorporate this requirement?
You could have two dimensions (date and location) and two fact tables (X and target KPI).
This is usually done in this way because the fact table with the real measure have more dimensions and rows, where the target/forecast has often less details.
I.e. A supermarket chain can fix monthly targets for each store, but it has sales data by store, day, product.
My suggestion is to have two separated tables.
Then if you will use often measures (from X table) and target KPIs together (showing deltas, delta percentage, etc.), then you can think about creating a third fact table (or a view, if performances allow that) to avoid joining between the other two fact tables every time you need that.
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 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.
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.