Category Archives: DAX

M vs DAX: Chopping Broccoli vs Planning a Menu

Last week, I had the pleasure of recording some video with Bert Wagner about Power BI. In the video, I got to use one of my favorite analogies for M versus DAX: Are you chopping broccoli or planning a menu?

One of the challenges with learning Power BI, is that you have to learn not 1, but 2 new data manipulations languages. And it’s not always clear what they are good for, especially if you come from the SQL world.

Is M a general purpose knife, or one of those weird egg slicers?

Head Chefs versus Sous Chefs

I have never worked in the restaurant business, but I’m going to make some gross generalizations anyway.

Sous chefs, as far as I can tell, do a lot of the prep work. They are cutting vegetables, cleaning food, making sauces, etc. While this is all important work, much of it doesn’t inform the final outcome. If you are making beef teriyaki or if you are making broccoli salad,  you still need to chop the broccoli.

The head chef however, gets paid for her brains just as much as her hands. The head chef is figuring out the menu and how to combine all of the ingredients. She is involved very heavily with what the final result is going to be. A head chef has to think of the broader goals and strategy of the restaurant, not just how to get the immediate task done.

M is the Sous Chef; DAX is the Head Chef

Again this is all a gross generalization, but in the restaurant called Casa De Meidinger this is actually the case! I do a lot of the grunt work when we cook a meal. My wife says, “zest this lemon” and I mindlessly do it. I could probably be replaced with a robot some day, and that would be fine by me.

Annie, however, actually enjoys planning a meal, deciding what to cook, and thinking about how to make the final product. To me, cooking is just a necessary evil for eating. I don’t necessarily get any joy from the process itself.

Working with M

I like to think of M as this sous chef. It does all the grunt work that we’l like to automate. Let’s say that my boss asks for a utilization report for all of the technicians. What steps am I doing to do in M?

  1. Extract the data from the line of business system
  2. Remove extraneous columns
  3. Rename columns
  4. Enrich the services table with a Billable / NonBillable column
  5. Generate a date table

This is all important work, but I would have to do the same work for a variety of reports. Many of the steps tell me nothing about the final product. I would generate a date table for most of my reports, for example.

Working with DAX

Now, if I’m working DAX, what am I going to do?

  1. Ask what the heck “utilization” really means

This was a real-life example that happened to me. What is utilization as a key metric? Well it turns out it depends what you are trying to report on. A simple definition is usage divided by availability. If a technician billed 20 hours and clocked in 40, his utilization would be 50%. Or so you would think.

How do we handle internal projects? Let’s say we have a technician who billed 2 hours to a customer, but spent 38 hours on an internal database migrations. What was his utilization?Well, if we are looking for billable utilization, it’s 5%. If we are looking for total utilization, it is 100%. These are questions that you are going to encapsulate in your DAX formulas.

The whole idea of a BI semantic layer is to hide away the meaning from the end users. When someone orders a cobb salad, they don’t want to have to articulate the ingredient list. They just want a darn salad.

Are you paid for your hands or your brain?

In the SQL Data Partners podcast, episode 114, there was a question: what’s the difference between a contractor and a consultant. One of the answers was this: a contractor is a set of hands, and a consultant is a set of brains.

I think this answer relates to M versus DAX. M is an automated set of hands, able to do work you’d normally do by hand in Excel. DAX let’s you take your domain knowledge and encode it into a data model. It’s an externalized representation for your brain.

And if you think about it, which do you want to be paid for? Do you want to get paid to unpivot data by hand every week? Or do you want to get paid for thinking, for understanding the business and for working at a higher level.

M allows you to automate the first step, so you can do more of the latter with DAX.

Why DAX is a PITA: part 1

  • So, I think that DAX is a pain in the butt to use and to learn. I talk about that in my intro to DAX presentation, but I think it boils down to the fact that you need a bunch of mental concepts to have a proper mental model, to simulate what DAX will do. This is very deceptive, because it looks like Excel formulas on steroids, but conceptually it’s very different.

Here is the problem with DAX, in a nutshell:


This example below is a perfect example of that sharp rise in learning curve, and dealing with foreign concepts like calculated columns, measures, applied filters, and evaluation contexts.

So, one of the things I’m hoping to catalog are example where DAX is a giant pain if you don’t know what you are doing. People make it look really simple and smooth, and that can be frustrating sometimes. Let’s see more failures!

How do I GROUPBY in DAX?

John Hohengarten asked me a question recently on the SQL Community Slack. He said:

I need to sum an amount column, grouped by a column
Measure 1 :=
    “Total AR Amt Paid calc”SUM ( det[amt] )
I’m getting a syntax error

So automatically, something seemed off to me. Measures are designed to return a single value, given the filter context that’s applied to them. That means you almost always need some aggregate function at an outer level. But based on the name, you wouldn’t necessarily expect GROUPBY to return a single value. It would return values for each grouping instance.

If we take a look at the definition for GROUPBY(), we see it returns a table, which makes sense. But if you are new to DAX, this is really unintuitive because DAX works primarily in columns and tables. This is a really hard mental shift, coming from SQL or Excel.

 What do you really want?

None of this made any sense to me. Why would you try to put a GROUPBY in a measure? That’s like trying to return an entire table for a KPI on a dashboard. It just doesn’t make sense. So I asked John what he was trying to do.

He sent me an image of some data he was working with. On the far left is the document id and on the far right is the transaction amount.
Pasted image at 2017_07_06 09_28 AM

He wanted to add another column on the right, that summed up all of the amounts for transactions with the same document. In SQL, you’d probably do this using a Window function with a SUM aggregate, like here.

 Calculated columns versus measures

This highlights another piece of DAX that is unintuitive. You have two ways of adding business logic: calculated columns and measures. The both use DAX, both look similar and are added in slightly different spots.

But semantically and technically, they are very different beasts. Calculated columns are ways of extending the table with new columns. They are very similar to persisted, computed columns in SQL. And they don’t care at all about your filters or front-end, because the data is defined at time of creation or time of refresh. Everything in a calculated column is determined long before you are interacting with them.

Measures on the other hand, are very different. They are kind of like custom aggregate functions, like if you could define your own version of SUM. But to carry the analogy, it would be like if you had a version of SUM that could manipulate the filters you applied in your WHERE clause. It gets weird.

My point is, if you don’t grok the difference between calculated columns and measures, you will never be able to work your way around the problem. You will be forced to grope and stumble, like someone crawling in the dark.

Filter context versus row context

So in this case we’ve determined we actually want to extend the table with a column, not create a free-floating measure. Now we run headlong into our next conceptual problem: evaluation contexts.

In DAX there are two types of evaluation contexts: row contexts and filter contexts. I won’t go too deep here, but they define what a formulas can “see” at any given time, and in DAX there are many ways to manipulate these contexts. This is how a lot of the time intelligence stuff works in DAX.

In this case, because we are dealing with a calculated column, we have only a row context, not filter context. Essentially, the formula can only see stuff in the same row. Additionally, if we use an aggregate like SUM, it only cares about the filter context. But the filter context comes from user interaction. Because this data is defined way before that, there is no filter context.

This is another area, where if you don’t understand these concepts you are SOL. Again, for the newbie, DAX is a pain.

 What’s the solution?

So what is the ultimate solution to his problem? There are probably better ways to do it, but here is a simple solution I figured out.

    SUM ( Source_data[Amount] ),
    ALL ( Source_data ),
Source_data[Document] = EARLIER ( Source_data[Document] )

Walking through it, The CALULATE is used to turn our row context, into a filter context. Then it manipulates that filter context so SUM “sees” only a certain set of rows.

The first manipulation is to run ALL against the table, to undo any filters applied to it. In this case, the only filter is our converted row context. (confused yet?)

The next manipulation is to use EARLIER (which is horribly named) to get the value from the earlier row context. In this case we are filtering ALL the rows, to all of them that have the same document. Then, finally we apply the SUM, which “sees” the newly filtered rows.

Here is what we get as a result:


 How do we verify that?

A fourth pain with DAX is that it’s very hard to look at intermediate stages of a process, like you can with SQL or Excel formulas, but in this case we have a way. If we convert our SUM to a CONCATENATEX, we can output all the inputs as a comma separated list. This gives us a slightly better idea of what’s going on.

image (1)

 What’s the point?

My point is, that DAX, despite it’s conciseness and richness is hard to start using. Even basic tasks can require complex concepts, and that was a big frustration point for me. You can’t just google GROUPBY and understand what’s going on.

Again, check out my presentation I did for the PASS BI virtual group. I tried to cover all the annoying parts that people new to DAX will run into. That and buy a book! you’ll need it.