Press "Enter" to skip to content

Category: Power BI

Flattening Dimensional Models

Published 2019-03-18 by Kevin Feasel

Reza Rad explains why it makes sense to build flat dimensional models, particularly for Power BI:

The article that I wrote earlier this week about the shared dimension had a lot of interest, and I’m glad it helped many of you. So I thought better to write about the basics of modeling even more. In this article, I will be focusing on a scenario that you have all faced, however, took different approaches. Is it good to have too many dimension tables? can you combine some of those tables together to build one flatten dimension table? how much should you flatten it? should you end up with one huge table including everything? In this article, I’m answering all of these questions and explaining the scenarios of combining dimensions, as usual, I explain the model in Power BI. However, the concepts are applicable to any other tools. If you like to learn more about Power BI; read Power BI book from Rookie to Rock Star.

Given how closely the ideal Power BI data model matches the Kimball model, Reza’s advice makes perfect sense.

Comments closed

Parameterized Direct Query In Power BI

Published 2019-03-12 by Kevin Feasel

Gerhard Brueckl shows how you can parameterize your SQL queries in Power BI, letting you switch server or database names and easily target a different location altogether:

I frequently work on projects where we have multiple tiers on which our solution is deployed to using continuous integration / continuous deployment (CI / CD) pipelines in Azure DevOps. Once everything is deployed, you also need to monitor these different environments and check the status of the data or ETL pipelines. My tool of choice is usually Power BI desktop as it allows me to connect to e.g. SQL databases very easily. However, I always ended up creating a multiple Power BI files – one for each environment.

Having multiple files results in a lot of overhead when it comes to maintenance and also managing these files. Fortunately, I came across this little trick when I was investigating in composite models and aggregations that I am going to explain in this blog post.

I have had to do exactly this same thing, so I’m going to have to try it out myself.

Comments closed

Showing Totals on Power BI Stacked Column Charts

Published 2019-03-12 by Kevin Feasel

Reza Rad shows us how to add a totals figure to Power BI stacked column charts:

As you can see, there are data labels for each subcategory (means gender and education), but no data label showing the total of each education category. for example, we want to know how much was the total sales in the High School category. Now that you know the problem, let’s see a way to fix it.

Read on for Reza’s solution to the problem. In general, if people might care about the total, do them a favor and show the total.

Comments closed

Power BI AutoML

Published 2019-03-06 by Kevin Feasel

Teo Lachev takes a look at AutoML in Power BI:

Let’s see how AutoML works based on what’s in the private preview (the usual disclaimer is that things will probably change). To start with, AutoML requires a dataflow (a note to Microsoft here is that AutoML will become more pervasive if it’s available in Power BI Desktop and it doesn’t require a premium capacity). In the private preview, AutoML requires the following steps. Presumably. the first (and most difficult step), preparing the dataset and cleansing the data is already done and available as a dataflow entity:

It looks like Microsoft’s taking what they learned from Azure ML and trying to port it over to Power BI.

Comments closed

Using the ALL Function in DAX

Published 2019-03-05 by Kevin Feasel

Reza Rad shows us how we can make use of the ALL function in a DAX query:

Among all the functions in DAX; the behavior of ALL function still seems mysterious for many. Many people, don’t use it at all and end up writing a very complicated calculation for a scenario that only one simple expression can do the same job. Some people, use it, but don’t exactly know how the function works, and get some unexpected results, and call it an error. In this article, I’m going to explain what ALL function is, how it can be used, and what are use cases of using such a function in DAX and Power BI. If you like to learn more about Power BI, read the Power BI book from Rookie to Rock Star.

Read on to see how the function behaves. Reza does a good job getting into the nuance of this function.

Comments closed

Converting One Column Into Multiple With Power Query

Published 2019-03-05 by Kevin Feasel

Imke Feldmann shows us how to pivot a single column into a fixed number of columns using Power Query:

The demand to unstacking a column into a table is not rare (see here for example: PowerBIForum  ) . Also if you copy a table from a post in the Power BI community forum  to the enter-data-section in Power BI, it will show up as such a one-column-table.

Note that this is different from the Entity-Attribute-Value model, as there’s no entity or attribute—it’s just values.

Comments closed

Which Power BI Visuals Send Data Externally

Published 2019-03-01 by Kevin Feasel

Meagan Longoria does a bit of investigation into data privacy and Power BI visuals:

One thing that makes understanding data privacy in custom visuals easier is the designation of a certified custom visual. One of the requirements for certification is ” Does not access external services or resources, including but not limited to, no HTTP/S or WebSocket requests go out of Power BI to any services.”

You can find the list of currently certified custom visuals on this page. Custom visuals are also identified in the marketplace by a blue star with a check mark.

Read on for some good investigative analysis.

Comments closed

Power BI IntelliSense For Python and R

Published 2019-02-26 by Kevin Feasel

David Eldersveld makes me wonder about the value of Power BI’s IntelliSense for R and Python:

If I type the letter a into the R Script editor, my code completion options are acts, always, and, and as. Power BI’s editor is not offering any IntelliSense options from a Python or R dictionary. Instead, it’s pulling from the text already in the editor. Note the comment in Line 1 and the inclusion of words beginning with the letter a — always, and, acts, as.

By comparison, the DAX editor contains a detailed function list and helpful annotations for code completion. Can we get something similar for R and Python? Not exactly… But there’s a workaround that I’m almost embarrassed to suggest. If you are a user who codes directly into the script editor, the following hack could be helpful. If you use the option to Edit script in External IDE, keep doing that and ignore the following guidance.

As-is, this is worse than no IntelliSense because at least with no IntelliSense, it’ll never steal a mouse click or keystroke. I wouldn’t expect RStudio level quality out of the gate but unless I’m missing something, that’s pretty bad.

1 Comment