A Fine Slice Of SQL Server
If you missed the first part of the series here is the link here: Query Performance – Part 1 Migrating Azure Analysis Services to Power BI Premium Per User – Reporting/Analytics Made easy with FourMoo and Power BI
In this blog post I am going to investigate how well does PPU scale when comparing it to AAS.
When comparing AAS to PPU, I must find the same size AAS size to what we get with PPU.
Read on for Gibert’s findings.
Comments closedEd Hansberry has the solution to a tricky problem:
It is fairly common for users to format Excel reports with headers that are comprised of two or more rows for the header, rather than using a single cell with word wrap on. I’ve seen text files with similar issues as well. Consider the following example:
Getting this info into Power Query can sometimes be a challenge. I’m going to show you two ways to do it. The first way will be to do it manually mostly using the Power Query user interface. The second way will be a custom function that will do all of the work for you. For simplicity’s sake, I’ll use Power Query in Excel for this since my data is in Excel already, but the same logic would hold if you were importing the data into Power Bi. We do not want to consolidate the headers in Excel. You’ll just have to do it manually again the next time you get a file from someone. Remember – never pre-transform your data before you transform it in Power Query.
The nice thing is that Power Query makes this tricky problem fairly easy to solve.
Comments closedBrett Powell gives us a list of things which might cause breaking changes in Power BI reports:
A breaking change, which we can define as any change to a dataset which causes either reports to render errors or the dataset to fail to refresh, can severely impact business workflows and reflect poorly on those responsible for the solution. Given significant investments in other areas of the organization’s data estate such as Azure Synapse Analytics, a simple, easily avoidable oversight in a Power BI deployment may not be tolerated.
Read on for the list.
Comments closedPatrick LeBlanc is looking for oddities:
Looking for ANOMALIES in Power BI? Justyna Lucznik walks Patrick through the new anomaly detection in Power BI and gives a SNEAK PEAK at what’s coming!
Click through for the video and demonstration.
Comments closedMatt Allington updates an older article:
Consider the example below where the CalendarYear is filtered for 2019 and the values of the measures Total Sales and Total Sales YTD are displayed by month. As you can see, the total sales are shown up to July 2019. This is because with the sample data, the last sales date is somewhere in July 2019. However, the values of Total Sales YTD are repeated all the way until the end of year (July 2019 to December 2019). This is what I call Calendar Over Run. It is common to want to prevent this overrun.
Read on for two separate methods of preventing this visual issue.
Comments closedPhil Seamark has an interesting problem:
The requirement was simple enough. Take the following dataset and, for any given day, produce a count of each possible State using the last known State for any given TestID. The dataset contains six unique Test IDs (A through F). At any given point in time, we first want to establish the last State for each TestID. We also want to group this by day and produce a count value for each possible State. Note, a given TestID can have more than one event in a day, and we only care about the last one.
I’m particularly interested in this because I find a lot of merit in the event-based structure in Phil’s input dataset, but it can be tricky going from that to data in a shape the customer likes.
Comments closedImran Burki brings us an introduction to Power BI Goals:
One of the things I love about Power BI (and Microsoft in general) is that they empower everyone in the organization to utilize their software – Power BI Goals are so easy to set up. There’s absolutely no special skillset required. You just need a Power BI Premium or Premium Per User license. Power BI Goals essentially enable you to keep track of key performance indicators in a single, unified view. Goals, and the actuals, are data driven. Goals can also be hardcoded. The data for actuals and goals must reside in a report that you can access.
Click through for an example as well.
Comments closedMarco Russo and Alberto Ferrari have another good use case for calculation groups:
Because the axis of the visual must show months outside of the slicer selection, we cannot use the usual Date[Calendar Year Month] column. Indeed, if we put the Date[Calendar Year Month] column on the X-axis, the only visible month would be the selected month. It is worth remembering that the selection of the values to show on the axis is independent from the measure. If a slicer is filtering one month, there is no way to show additional months from the same table on either the rows or the columns (or the X-axis, as in the example).
Therefore, we must create a separate table that is not subject to filtering from the slicer. This way, columns from that table show all the rows, and we can control their visibility through a measure. Once the new table is in place, we write a measure that produces a value for only the last six months out of all the months visible, and leaves the remaining months blank in order to hide them.
Read on to see how.
Comments closedIn part 1, I gave you an introduction to Power BI Goals and provided some context on what information could be tracked with the feature. I received some feedback that it would be great to do an “unboxing” post, to get a look at the feature. Thus, this post will be that. Part 3 will be a Power BI Goals 101 post where I step you through the process.
Power BI Goals requires you to either have a Power BI Premium per user license or to have access to Power BI Premium and have a Power BI Pro license assigned to you. If you do, then you will notice a new trophy icon when you go to PowerBI.com.
Click through for the overview.
Comments closedGilbert Quevauvilliers picks back up on a new series:
Welcome to the first in my blog post series on evaluating the different aspects when looking to migrate from Azure Analysis Services (AAS) to Power BI Premium Per User (PPU).
Apologies for this taking a few extra weeks to get started, life has been super busy, but as they say “Better late than never”.
In this post I am going to compare the Query Performance of an AAS Cube compared to a PPU Cube.
Click through to see how Power BI Premium Per User stacks up against Azure Analysis Services.
Comments closed