Category Archives: DAX

Power BI Learning Path – Free and Paid Resources

This week’s TSQL Tuesday challenge is on learning something other than SQL. I’ve written before about how to keep up with technology. When you are starting out with a technology, it’s just plain hard to get a lay of the land.

So I thought I’d put together a learning path for Power BI, a technology that changes literally every month. This is a bit of challenge because there are so many moving parts when it comes to Power BI. So let’s break down those moving parts into different categories.

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So, when I think about Power BI, I like to think about the flow of data. First we have the Data prep piece with Power Query, where we clean up dirty data. Next we model the data with DAX. I’ve written before about the difference between Power Query and DAX. They are like peanut butter and jelly and compliment each other well.

Now, if you are a SQL expert, you may not need to worry about Power Query or DAX much. Maybe you do a lot of the work in SQL. But either way, once your data is modeled, you need to visualize it in some way. You need to learn how to create your reports with Power BI Desktop. Once your report is created, you then need to publish it.

Finally, there is what I would call the IT Ops side of Power BI. You have to install an on-premises data Gateway to access local data. You need to license your users. You need to lock down security. All of these things might be outside of what a normal BI developer has to deal with, but are still important pieces. However, unlike the data flow model we talked about, the ops pieces happens at all of the stages of development and deployment.

With that overview in place, let’s get on to the individual sections and the learning paths as a whole.

Getting started with Power BI

When it comes to getting started with Power BI, I have two recommendations. First get your hands dirty, and secondly buy a book. Power BI is in many ways an amalgamation of disparate technologies. It took me a long time to to understand it and it didn’t really click until I took the edX course and did actual labs.

The reason I say to buy a book is this is a technology that is hard to learn piecemeal. When you are starting out you are much better off having a curated tour of things.

Free resources

  • Check out Adam Saxton’s getting started video.
  • Search Youtube for Dashboard in an Hour. This is a standardized presentation that will show you the basics in under an hour.
  • Follow the guided learning. This will walk you through bite sized tasks with Power BI.
  • Take the edX course. It has actual labs where you have to work with data inside of Power BI.
  • Check out the Introducing Microsoft Power BI book from Microsoft Press. It’s a bit dated at this point, but it’s free and is a great start.
  • Check out the Power BI: Rookie to Rockstar book from Reza Rad (b|t). The last update was July 2017, but it’s also very comprehensive and good.

Paid resources

  • Stacia Misner Varga (b|t) has a solid course on Pluralsight. It’s worth a watch.
  • Consider reading the Applied Power BI by Teo Lachev (b|t). It’s a real deep dive which is great, but can be a lot to take in if you are just getting started. A neat feature is that it’s organized by job role.

Learning Power Query and M

When it comes to self-service data preparation, Power Query is THE tool. The way I describe it is as a macro language for manual data manipulations. If you can pay someone minimum wage to do it in Excel, you can automate it in Power Query. Again, check out this post for the differences between Power Query and DAX.

Free Resources

  • Start with the guided learning. This quickly covers the basics
  • Reza Rad has a solid getting started post on Power Query that you can follow along with.
  • Matt Masson has a phenomenal deep dive video on the Power Query formula language, a.k.a M, from a year ago. It really helps elucidate the guiding principals of Power Query and M.
  • Blogs to check out:
    • Imke Feldmann (b|t) regularly has complex functions and interesting transformations on her blog.
    • Ken Puls (b|t) focuses on Excel and along with that, Power Query.
    • Gil Raviv (b|t) often has neat examples of things you can do with Power BI and Power Query.
    • Chris Webb (b|t) regularly dives into the innards of Power Query and what you can do with it.

Paid Resources

  • Ben Howard (b|t) has a Pluralsight course on Power Query. It’s a bit introductory, but great if you are just getting started.
  • Gil Raviv recently (October 2018) released a book on Power Query. What I really like about this book is it has more of a progression style instead of a cookbook kind of feel.
  • Ken Puls and Miguel Escobar (b|t) also have a book on Power query that has a cookbook feel. I found it helpful in learning Power Query, but it’s heavily aimed at excel users.
  • Finally, Chris Webb also has a book on Power Query. He goes into a lot of detail with it. However, the 2014 publish date means it’s starting to get a bit old.

Learning DAX

I always say that DAX is good at two things: aggregating and filtering. You aren’t doing those two things, then DAX is the wrong tool for you. DAX provides a way for you to encapsulate quirky business logic into your data model, so that end users doing have to worry about edge cases and such.

Free Resources

  • Read the DAX Basics article from Microsoft
  • Check out the guided learning on DAX
  • Learn the difference between Calculated columns and Measures in DAX. They can be confusing.
  • Make sure you understand the basics with SUM, CALCULATE and FILTER
  • Understand Row and Filter contexts. They are critical for advanced work in DAX
  • Blogs to check out
    • Matt Allington (b|t) has a blog with Excel right in the name but also writes about all the different parts of Power BI Desktop.
    • Rob Collie (b|t) has a voice all his own. read his blog to learn about DAX and PowerPivot without taking yourself too seriously.
    • Alberto Ferrari (b|t) and Marco Russo (b|t) are THE experts on DAX. Read their blog. Also see their site DAX.guide.
    • Avi Singh (b|t) regularly posts videos on Power BI and will often take live questions.

Paid Resources

Power BI Visuals

The piece of Power BI that is most prominent are they visuals. While it’s incredibly easy to get started, I find this area to be the most difficult. If you are heavily experience in reporting this shouldn’t be too difficult to learn.

Free resources

Paid resources

  • A really interesting book is The Big Book of Dashboards. While it doesn’t mention Power BI, it covers all the ways to highlight data and what really makes a dashboard.

Administering Power BI

Power BI is much more than a reporting tool. It is a reporting infrastructure. This means at some point you may have to learn how to administer it as well.

Free resources

Paid resources

Keeping up with Power BI

One of the big challenges with Power BI is just keeping up. They release to new features each and every month. Here are a few resources to stay on top of things:

Going Deeper

Finally, you may want to go even deeper with things. Here are some final recommendations:

#SQLChefs: Power BI Datasets, Reports and Dashboards

This week we’ve got another episode of SQLChefs with Bert Wagner, where we talk about the different between datasets, reports and dashboards in Power BI.

What are datasets?

A Power BI Dataset is a series of Power Query queries that have been shaped in a DAX model. Each dataset can combine different files, database tables and online services all into one tabular model.  In our cookie analogy, these are all different “ingredients”.

Unlike SSRS, a dataset in Power BI does not represent a single table or query of data. A dataset should be considered more like a “flavor” of data used to accomplish a specific type of reporting: financial, operational, HR, etc. So in our analogy, the dataset is the “raw dough”.

So in Power Query, you are going to have a set of queries which each combine a data source with a usually linear set of transformations.

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Then, in DAX, you are going to take each of those outputs and combine them into a model. This consists of defining relationships between the outputted tables and adding business logic via calculated columns and measures.

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For more on the difference between Power Query and DAX, see our previous episode of SQLChefs.

What are reports?

A power BI report is a series of visualizations, filters and static elements on a canvas. Power BI reports are saved as a single PBIX file and connect to a single dataset. Remember, a Power BI dataset can have many data sources.

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(Demo file courtesy of Microsoft, MIT License)

Each report can have multiple sheets, just like an Excel workbook. In our analogy, this is us placing our “cookies” on multiple “cookie sheets” making one big batch, all of the same “flavor”.

One report per dataset

A quick aside to something that used to confuse me. In most cases, a report and a dataset are going to have a one to one relationship. A dataset can have one report and a report can have one data set.

Recently this has changed, however. A while back, they added the ability to use an existing dataset as a data source for a report. and at Ignite they announced the ability to share datasets outside of the app workspace they were made in.

That being said, while you are still learning Power BI, it’s easier to remember that in many cases, your dataset and your report are going to have a one-to-one relationship and be tightly linked.

What are dashboards?

In Power BI, dashboards are a way of pulling together visualizations from various reports. When you think dashboard, you are probably thinking something like Microsoft’s definition: “A Power BI dashboard is a single page, often called a canvas, that uses visualizations to tell a story. Because it is limited to one page, a well-designed dashboard contains only the most-important elements of that story.”

However, if you look at the report example above, it probably fits that definition. It is not a Power BI Dashboard. In Power BI, a dashboard is tool for pinning visuals from different reports and other sources of data.

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In my opinion, a Power BI Dashboard is as much a tool for organization and navigation, as it is for actual reporting. I think that’s the real value add with Power BI dashboards.

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:

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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 :=
GROUPBY (
det,
det[nbr],
    “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 =
CALCULATE (
    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:

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 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.

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 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.