There is now a new tenant setting for Power BI admins to prevent users from creating classic workspaces in Power BI! This includes from places like MS Teams! Hurrah!
If you’re a Power BI Admin you can now enable ‘Block classic workspace creation’.
To do this go to the powerbi.com portal and click the ‘settings’ icon in the top right hand corner of the screen.
Click on ‘Admin portal’ and then locate and click ‘Tenant settings’.
Next under the ‘Workspace settings’ click ‘Block classic workspace creation’.
Now change the toggle from ‘Disabled’ to ‘Enabled’. This will now block the creation of new classic workspaces in your Power BI portal from places like MS Teams.
This will also automatically remove classic workspaces from your Power BI workspace list, but only if they were created by Teams, and only if they’ve never been accessed. If they have been accessed then they’ll be left there for you to deal with manually.
Now you’re probably thinking well I’ll just delete any old remaining workspaces which have been accessed but are no longer being used! Woah hold on…If you do this you’ll end up deleting the underlying office 365 modern group (and the associated Team!) which still may be used.
A better way of doing this is to upgrade the workspace to the new experience and then delete it.
To do this, click on the ellipsis next to the filters button within the workspace you want to delete and click ‘Edit workspace’. Next go to the advanced section and under ‘Upgrade this workspace (preview)’ select ‘Upgrade now’.
A small window will appear in the middle of your screen.
Check the ‘I’m ready to upgrade this workspace’ option and then click ‘Upgrade’.
Now, once the workspace has been upgraded to the new experience you can then click on ‘Settings’ icon and click ‘Delete Workspace’. This will now leave the office 365 modern group untouched but remove it from your workspace. Simple!
As always a big thanks to the guys from at Guy In A Cube (Twitter: https://twitter.com/GuyInACube) on You Tube for sharing this knowledge!
Check out their video here: https://www.youtube.com/watch?v=T2PAL4D2SvU.
Here’s the link to the Microsoft Power BI team’s announcement on this new feature: https://powerbi.microsoft.com/en-us/blog/announcing-you-can-now-block-classic-workspace-creation/
In this blog post we’ll take a quick look at using ConcatenateX function to view a concatenated string of dates where the max daily sales occurred for a given month.
I came across this function whilst going through the excellent “Mastering DAX 2nd Edition Video Course” by the guys from SQLBI.com. So credit to Marco and Alberto for sharing this.
So how does it work? If we had a list of dates ranging from 01/01/2020 to 31/12/2020 and we wanted to see which days we achieved maximum sales for each given month in a year we could use the ConcatenateX function to return these dates in a single row per month.
As we can see in the screenshot below, the left hand table shows the month of June where we achieved maximum sales for in June on both 18/06/2020 and 25/06/2020 of 99. In the table to the right we can see those two dates presented on a single row for the month of June in the column “What were the max days?”. This was column was created using the ConcatenateX function!
So let us first look at what the maximum daily sales were per month. To do this we’ll use the MAXX function to create “Max Daily Sales”. This returns the maximum daily sales rate achieved for each given month as a single value. So for the month of June this would be 99. The problem with this is we are not sure which days these max sales were achieved on without drilling down into the data. Was this just one day or was it multiple days? All we can see is a figure of 99.
So let us create a new measure to work out on which days this figure of 99 occurred on.
The variable at point (a) returns a table with a single column which lists all of the unique dates in our Sales_2020 table.
The variable at point (b) returns the max sales for the given filter context in this case month.
The variable at point (c) uses the filter function to filter out only the days where the max sales were achieved by setting the total quantity sold measure to the max daily sales variable. For example in June we achieved max daily sales of 99 on 18/06/2020 and 25/06/2020. Therefore the variable at point (c) would filter out the ListOfDays table variable to just 18/06/2020 and 25/06/2020 only.
If we just had one max sales day per month we could simply return MaxDaysOnly. However we may have multiple days per month where max sales were achieved. Hence we use the ConcatenateX function to create a string of dates.
The variable at point (d) creates a string of concatenated dates separated by the delimiter “,” which can be used against a single row in a table.
Wrapping it all up returns us this table below, which shows us which days max sales were achieved per given month! Pretty cool eh?
Check out https://www.sqlbi.com/articles/mastering-dax-video-course-2nd-edition/ for more information regarding the online DAX course as well as https://twitter.com/marcorus and https://twitter.com/ferrarialberto on Twitter!
By Nick Edwards
In this blog post we’ll take a quick look at creating a self-generating calendar table using DAX.
Dates are important if we want to perform time intelligence reporting on our data i.e. yearly sales, monthly sales, weekly sales, year to date sales or previous year sales.
We’ll be using the calendar function to create our date table, but there are other methods to do this such as CALENDARAUTO or GENERATESERIES.
Here is the syntax we’ll be using to generate our date table.
The calendar function returns a single column called “Date” which generates a continuous series of dates from the specified start date to the specified end date. So if we specified the start date to be the 01/01/2020 and the end date to be 31/12/2020 the function would generate 366 rows of distinct date data.
We can then use the add columns function to expand our calendar table further to add specific columns we wish to slice our data by i.e. year, month, quarter, week number, day…
Here is the syntax we will be using to expand our calender table.
Let’s use an example to further explore how these functions work in practice using some sample adventure works sales data. Here we use the “Get Data” icon to directly query our adventure works database and bring over a sample of sales data, named “AW_Sales”.
Now, to create our calendar table we need to click “New Table” in the modeling tab and enter the following function.
Note: I like to use variables in my date table just to keep the DAX looking clean and fuss free, but this isn’t necessary.
For the start date parameter we have used the FIRSTDATE() and for the end Date parameter we have used the LASTDATE() function. This is so we can extract the first and last “OrderDate” from our AW_Sales table. We could have also used the MIN() and MAX() function to deliver the same results using the newly created “Date” column. As we can see this has generated a sequential date list from the 01/01/2012 to the 31/12/2013 with 731 distinct rows of data.
Now we want to expand our calendar table using the newly created “Date” column with new columns which can slice and dice our data. To do this I use the ADDCOLUMNS function. Here I have added Year, Quarter, Year Month, Month Number, Month Name, Day Of Year, Day Of Month, Day Of Week and Day Name as columns.
Once we’ve done this, we will mark our newly created table as a date table to allow Power BI to recognize date hierarchies and time intelligence functions.
We can now view our newly generated calender table in the data view. Instantly when any new data enters our model from “AW_Sales”, the calendar table will expand accordingly due to the last date function used above.
Now it’s just a case of creating a one to many relationship between our new calendar table and our “AW_Sales” table. We’ll create a one to many relationship between “Calendar[Date]” and “AW_Sales[OrderDate]” as shown below.
Congratulations we have now created a fully fledged calendar table that can slice and dice our “AW_Sales” tale by any of the columns we have created in our calendar table, as shown in the example below.