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Category: DAX

Calculating Last Year to Date with DAX

Reza Rad wants to compare prior years to the current year, using year-to-date comparisons:

I have previously written about how to calculate the same period last year calculation and compare this year’s values with the last year’s values. However, sometimes, you don’t yet have the full year, especially for the current year. You might want to compare this year’s value with the last year’s value up until the same day but last year. This is what I call same period last year to date. Here in this blog article, I’ll explain how you can do that using DAX in Power BI. To learn more about Power BI, read Power BI from Rookie to Rock Star.

Click through to see how it’s done.

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Analyzing DAX DISTINCTCOUNT Performance

Marco Russo takes a look at how fast DISTINCTCOUNT is in DAX:

This article shows how to implement the same DISTINCTCOUNT calculation in two alternative ways, measuring and comparing the performance in different reports. You will see that while DISTINCTCOUNT can be implemented using SUMX / DISTINCT, the DISTINCTCOUNT version is usually better. That is, unless the density of the reports is high and the calculation apply filters to the measures that do not correspond to the grouping granularity of the visualization – as is always the case using time intelligence functions. There are cases where SUMX / DISTINCT can offer better performance, but you have to clarify whether optimizing one report might slow down many others. Measuring performance using DAX Studio is the only way to know what to expect for your model and reports.

Read on for the full test.

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Seeing Filter Results with DAX Measures

Gilbert Quevauvilliers wants to see the specific values involved in a Power BI filter:

I was working with a customer trying to get them to better understand DAX and one if the things that is difficult to understand is how the filtering works in DAX.

The challenge I have found is that when using filters in a DAX measure I cannot visually see what is happening within the DAX Filter.

Below I will show you how I can see the values in the FILTER

Click through to see how.

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Power BI Column Concatenation

Alexander Arvidsson shows how we can concatenate columns in Power BI using DAX:

Finally we can tackle the last hurdle – the column that shows both the current number of certifications and the requested goal. Had this been Excel it would have been dead easy – we just create a new cell that concatenates two other cells like this:

Then we copy the formula to all the rows. Easy. But this is not Excel. The “goal” part is simple – that’s just another column. The trick is to count all the other rows in the table with the same key. Let’s add the key column to the table so we see what we’re working with. “CompKey” is the concatenated key we created in the previous blog post. “Number of certs” is a count of the rows in the table, and because of row context it gets evaluated per key.

Read on for the solution.

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Naming Temporary Columns in DAX

Marco Russo and Alberto Ferrari team up to share a standard for naming temporary columns in DAX:

The formula works just fine, but it violates one of the golden rules of DAX code: you always prefix a column reference with its table name, and you never use the table name when referencing a measure. Therefore, when reading DAX code, [Sales Amt] is a measure reference, whereas ‘Product'[Sales Amt] is a column reference.

Nevertheless, in our DAX example ProdSalesAmt is a column of a temporary table (SalesByProduct) created by the FilteredSalesAmount measure. As such, ProdSalesAmt is a temporary column that does not originate from any column in the model and does not have a table name you can use as a prefix. This situation creates ambiguity in the code: it is not easy to discriminate between a column reference and a measure reference. Therefore, the code is harder to read and more error prone.

Read on for their standard, which is pretty easy to follow.

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Matt Allington takes us through the REMOVEFILTERS() function in DAX:

Recently Microsoft introduced a new function in DAX called REMOVEFILTERS(). This is a very useful and well named function and it does exactly what its name suggests. Its purpose is to act as a table filter parameter inside CALCULATE() as shown in the following example.

Total Sales All Products REMOVEFILTERS() = CALCULATE([Total Sales],REMOVEFILTERS(Products))

Read on to see how this compares to the prior/alternative solution and for more information on REMOVEFILTERS().

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Divide, RankX, and N/A

Rob Collie has some fun with DIVIDE():

A blank cell in a report is sometimes a source of confusion for those human beings consuming our work. “What does a blank cell mean,” they ask.  “It’s a division by zero,” we reply.  “Wut,” they then ask.  “Trust me,” we say, “you don’t want to see the alternative.”  “But I don’t trust you, and now I don’t trust this whole report,” is what they sometimes say next – whether under their breath or out loud.

But “N/A” is a lovely value to display.  It raises far fewer eyebrows.  “Oh, it says our Profit Margin % for electric blankets sold in Cancun is “N/A” – I get it, we’ve never sold that product there.”  No convo required.

Click through for the full story.

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Modeling Semi-Additive Measures

Paul Poco shows a couple techniques for modeling semi-additive measures in Analysis Services and Power BI:

As mentioned earlier, the most commonly encountered approach is Option 2, the snapshot fact table. The main drawback of this approach is that the fact table’s size will grow extremely fast. For example, if you want to calculate the headcount in a company with 10,000 employees on average, and you want 5 years of historical data, you will add 10,000 rows per day to your fact table – that gives you (10,000 * 365 * 5 =) 18,250,000 rows after 5 years.  

If you used the first approach, Option 1, the fact table would be (10,000 * 5 =) 50,000 rows after 5 years, assuming your employees change position or quit the company once a year, on average. 

The snapshot fact table (Option 2) is (18,250,000 / 50,000 =) 365 times bigger. On the bright side, as the data is very repetitive, you might get a very good compression ratio on these tables.  

Check it out. Semi-additive measures are not as common as additive measures, but you’re liable to have a couple of them in your data model.

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