I am new to DAX and am pulling Tabular Data from Analysis Services. There is a lot of data and I only want to pull certain columns from different tables in the cube. I can pull a couple of columns from one table but not sure how to combine it. Please see the screenshot below any help with writing this query would be appreciated.
For example, I want to see [Region] from 'Companies' and [State] from 'Houses'
You should find the fact table related to Houses and Companies tables. Lets assume it's name is Fact, then you may use SUMMARIZE to retrieve just the combinations of the columns that exists in Fact table like follows
EVALUATE
SUMMARIZE(
'Fact',
'Houses'[State],
'Companies'[Region]
)
Related
I am a PowerBi beginner and would like to know if it is possible to create a table report that behaves like explained in the attached picture (according to the fact table I have).
I tried to do it with a DAX measure using the following formulas but it does not work as I wish :
CALCULATE(SUM('Fact'[Invoice]),'Fact'[Customer] = SELECTEDVALUE('Fact'[Customer]))
CALCULATE(SUMX('Fact','Fact'[Invoice]),'Fact'[Customer]=SELECTEDVALUE('Fact'[Customer]))
The slicer does not filter the table in order to get all the product in the table even if the customer did not buy one.
Is it possible to do it ?
Many thanks
I am trying to create a table that is a list of all possible combinations between two tables: products and companies. I'm building a dashboard in Power BI and the data comes from SQL queries. I have the following list structures for the Products table and the Companies table:
and my desired output for analysis is:
There is nothing that relates the companies to the products, and I'm trying to get a list of all of the products for each company. Can I do this in Power BI? If not, is it possible in SQL (there is nothing to join on)? Thank you for your help!
You can do this with a 'hacked' join, or a cross join. I prefer the former from a process POV, but cannot speak to speed or efficiency.
Using a Join ( Merge in PQ )
Create a new column 'DummyKey' with a value of 1 on each table.
Merge both tables using your 'DummyKey' columns.
Complete the Join process and choose the columns you want to bring through.
Cross Joins in PowerQuery according to MS
I think this 'cross join' is only technically right and does not provide future flexibility.
Both methods will get you to the same end point, and can be done in SQL or PQ.
I have a table visualisation that shows the populations of countries and a toggle switch that flips between 'sold' and 'unsold'. (This works with a measure that checks is a country is present in a sales table and assigns a 1 or 0 which is then used as a filter on the table visualisation).
Various slicers in the dashboard are used to filter the data model and retain the details of sales. When 'unsold' is selected therefore, the relevant countries are already filtered out of the countries data table and it is not possible to display them with their populations.
At the moment the workaround is to use a duplicate countries table that only has a one way filter, so that the rows remain regardless of filtering. This means that other slicers which interact with the rest of the data model don't filter the table visualisation as desired.
I am sure this must be possible using some combination of CALCULATE(), FILTER() and ALL() but I haven't managed to achieve this.
N.B. I can force the unsold countries to appear in a table visualisation using a constant measure (with formula: measure_name = 0) in a column .
Apologies if this is not very well explained, any help much appreciated.
Thanks for reading,
S
Image attached to (hopefully!) explain problem better.
Real scenario is more complicated hence not screenshotting from PBI.
In Power BI, I am creating a report with some finance data of a company. I have 3 different tables. The table structure of all three tables are as follows:
I want to change these tables into this structure:
Is it possible to achieve this kind of structure? If yes and please suggest some method to do this?
To do it simply you can import 3 times your table using the query editor, and then in one table keep only columns for Planned, in the second table keep columns for Actual, and so on...
Hope that helps!
I'm trying to figure out how to get a measure to adhere to the filter set by a slicer in Power BI.
My DAX query is: Block Time Cost = SUMX( FILTER(v_Invoice_Line_Items, v_Invoice_Line_Items[IV_Item_RecID]=9), v_Invoice_Line_Items[billable_ext_price_amount])
I know very, very little about DAX so my initial query may be way off base.
It calculates as I expect, but when filter with a date range silcer the value does not update as expected or at all.
I'm pulling my data from two views in the same database, v_Invoice and v_Data_Combined. I have a page level filter on the row Record_Description to limit the data to the types I'm looking for and the measure pulls it's data from rows in the v_Data_Combined view.
The rows in v_Invoice are below.
A sample copy is here.
and the rows for v_Data_Combined, if you click they will enlarge.
A sample copy is here.
I have no relations set between the views.
How can I have a measure adhere to slicer filters?
The slicer has to be on the same table as the measures you're filtering, or on a table related to that table. If your slicer is on a column in v_Invoice and your data is from v_Data_Combined - and the 2 tables are unrelated in Power BI, the slicer from one table will have no effect on the data from the other table.
Without sample data (which can be fake data), it's hard to make further recommendations.
However, if the two tables you have aren't really related to each other, then I would recommend exploring the possibility of "lookup" tables. E.g. if you have Company_Name in both tables, then you might add a 3rd table that is a unique list of companies (their name, address, etc). Then, when you want to slice by company you would slice on this 3rd table. That slicer will then filter both related tables (without having to have the tables related to each other).
You can read more about data modeling in Power BI, and how to design lookup tables, here: https://powerpivotpro.com/2016/02/data-modeling-power-pivot-power-bi/