A Fine Slice Of SQL Server
Gilbert Quevauvilliers needs a simpler method for navigation:
I was working with a customer where they had a lot of measures which they wanted to use with the awesome Field parameters for measures.
The challenge was that there was a LOT of measures, and I wanted to see if there was a way to create a hierarchy in my field parameters so that it would be easier to find the measure.
Click through to see how you can group field parameters.
Comments closedNikola Ilic tries something different:
Essentially, the idea here is, since these four lines are not changing based on the numbers in the visual itself (they have constant value based on the slicer selection), to leverage the Constant line feature from the Analytics panel. Since no Constant line is available with Line and clustered column chart visual, let’s duplicate our visual and change its type to a regular Clustered column chart.
This is a good reminder that the best outcome isn’t always the most straightforward one.
Comments closedRichard Swinbank configures some deployments:
In earlier posts in this series, I talked about to developing and deploying standalone Power BI datasets and automating report deployment into different environments. I’ll bring together those approaches in this post, to enable deployment of shared datasets into multiple environments. This has consequences for automated report deployment, and I’ll take a look at that too.
Read the whole thing.
Comments closedChris Webb has had a busy month:
Maybe the fourth- or fifth-most exciting Power BI-related announcement last month (admittedly it was an exciting month) was that Log Analytics for Power BI datasets is now GA and you can now link multiple Power BI workspaces to a single Log Analytics workspace. This, for me, means that enabling Log Analytics has gone from being useful to essential for anyone interested in monitoring Analysis Services engine activity in an enterprise Power BI/Fabric deployment. It also works with Direct Lake datasets too!
Read on for a few KQL queries which allow you to get pertinent information from your Log Analytics workspace.
Comments closedMarco Russo and Alberto Ferrari build a regression:
LINEST and LINESTX are two DAX functions that calculate a linear regression by using the Least Squares method. Both functions return multiple values, represented in a table that has a single row and one column for each of the values returned.
LINEST gets column references as arguments, whereas LINESTX explicitly iterates over the table provided in the first argument and executes the other arguments in a row context. Internally, LINEST invokes LINESTX and provides to it the table that contains the column references specified in the LINEST arguments. This article describes the more generic function LINESTX.
This is pretty neat for adding a visual element, though I’d probably include an R or Python visual and do the regression in there, myself.
Comments closedJason Cockington digs into the two modes:
Over the last few months, I have received a number of very similar questions at either one of our Live Training courses or from a student attending one of our Ask Anything Power BI Q&A sessions. These questions all revolved around DirectQuery and were often specifically about performance issues or complexity of DAX calculations being written on the Power BI reports. This prompted me to write this post, as it became abundantly clear that most students were opting for DirectQuery when they should have been selecting Import. If you are not sure if DirectQuery is right for you, then read on, as we explore further.
Jason includes some Real Talk from Chris Webb, fairly eviscerating DirectQuery mode in the majority of cases.
Comments closedKurt Buhler provides a nice graphic:
I’m just writing this quick article to share a visual overview I made of the newly announced Power BI-related features. I hope it helps you get an at-a-glance overview of some of the big changes relevant to Power BI. More importantly, I hope it doesn’t make you feel overwhelmed! For more information, check out the documentation and learning paths.
There is a lot in store for the platform, but you can already see a slew of new changes and opportunities for Power BI developers.
Comments closedMeagan Longoria uses a custom label:
Notice the label on each column that shows the year and average game length in h:mm format.
These custom labels are available for any bar or column chart – small multiples are not required.
Click through to see how.
Comments closedSteve Jones performs a comparison:
I ran across an interesting post from Rita Fainshtein that looked at the different types of graphs for a set of data. I thought that was interesting, so I ran my own experiment. I found for my data, a line graph was better, but let me know what you think.
My data set was simple, a few players across a few events and their number of kills. I coach volleyball and I’m always trying to present stats in a useful way. Here was the small set I picked.
Something to keep note of is that line charts generally imply time series data: you are looking at some periodic activity and analyzing changes between periods.
The stacked column (or bar) chart tells you two things: the total and the first event. It’s really hard to discern any other events from a stacked chart comparison, as Steve points out.
If you don’t have periodic data or if you’re more focused on general trends than what happened at a specific event, the Cleveland dot plot (available in custom visuals) could plot each of the events, with the Y axis representing player names and the X axis representing number of kills, and each dot being a specific game. This saves a lot of real estate and gives you an idea of how points cluster, especially if you look at a larger number of games or players.
Comments closedRichard Swinbank re-uses a pipeline:
Implementation of one pipeline per report makes additional demands of a developer when creating a new report. To make this easier to manage, in this post I look how to make pipeline creation as simple as possible, by building each pipeline from a set of reusable components.
Click through to see how this works in Azure DevOps. I’d expect the process to be reasonably similar for GitHub Actions as well.
Comments closed