Tag: SSAS

Azure Analysis Services, BISM Tabular, Business Intelligence - BI, Data Warehousing - DW, DAX, Power BI, Power BI Desktop, Power BI Desktop RS, Power Pivot, Power Query, Quick Tips, Self-Service BI, SQL Server, SQL Server Analysis Services - SSAS, T-SQL

Quick Tips: Time Dimension with Time Bands at Seconds Granularity in Power BI and SSAS Tabular

Time Dimension with Time Bands at Seconds Granularity in Power BI and SSAS Tabular

I wrote some other posts on this topic in the past, you can find them here and here. In the first post I explain how to create “Time” dimension with time bands at minutes granularity. Then one of my customers required the “Time” dimension at seconds granularity which encouraged me to write the second blogpost. In the second blogpost though I didn’t do time bands, so here I am, writing the third post which is a variation of the second post supporting time bands of 5 min, 15 min, 30 min, 45 min and 60 min while the grain of the “Time” dimension is down to second. in this quick post I jump directly to the point and show you how to generate the “Time” dimension in three different ways, using T-SQL in SQL Server, using Power Query (M) and DAX. Here it is then:

Time Dimension at Second Grain with Power Query (M) Supporting Time Bands:

Copy/paste the code below in Query Editor’s Advanced Editor to generate Time dimension in Power Query:

let
Source = Table.FromList({1..86400}, Splitter.SplitByNothing()),
#"Renamed Columns" = Table.RenameColumns(Source,{{"Column1", "ID"}}),
#"Time Column Added" = Table.AddColumn(#"Renamed Columns", "Time", each Time.From(#datetime(1970,1,1,0,0,0) + #duration(0,0,0,[ID])), Time.Type),
    #"Hour Added" = Table.AddColumn(#"Time Column Added", "Hour", each Time.Hour([Time]), Int64.Type),
    #"Minute Added" = Table.AddColumn(#"Hour Added", "Minute", each Time.Minute([Time]), Int64.Type),
    #"5 Min Band Added" = Table.AddColumn(#"Minute Added", "5 Min Band", each Time.From(#datetime(1970,1,1,Time.Hour([Time]),0,0) + #duration(0, 0, (Number.RoundDown(Time.Minute([Time])/5) * 5) + 5, 0)), Time.Type),
    #"15 Min Band Added" = Table.AddColumn(#"5 Min Band Added", "15 Min Band", each Time.From(#datetime(1970,1,1,Time.Hour([Time]),0,0) + #duration(0, 0, (Number.RoundDown(Time.Minute([Time])/15) * 15) + 15, 0)), Time.Type),
#"30 Min Band Added" = Table.AddColumn(#"15 Min Band Added", "30 Min Band", each Time.From(#datetime(1970,1,1,Time.Hour([Time]),0,0) + #duration(0, 0, (Number.RoundDown(Time.Minute([Time])/30) * 30) + 30, 0)), Time.Type),
#"45 Min Band Added" = Table.AddColumn(#"30 Min Band Added", "45 Min Band", each Time.From(#datetime(1970,1,1,Time.Hour([Time]),0,0) + #duration(0, 0, (Number.RoundDown(Time.Minute([Time])/45) * 45) + 45, 0)), Time.Type),
#"60 Min Band Added" = Table.AddColumn(#"45 Min Band Added", "60 Min Band", each Time.From(#datetime(1970,1,1,Time.Hour([Time]),0,0) + #duration(0, 0, (Number.RoundDown(Time.Minute([Time])/60) * 60) + 60, 0)), Time.Type),
    #"Removed Other Columns" = Table.SelectColumns(#"60 Min Band Added",{"Time", "Hour", "Minute", "5 Min Band", "15 Min Band", "30 Min Band", "45 Min Band", "60 Min Band"})
in
    #"Removed Other Columns"
Continue reading “Quick Tips: Time Dimension with Time Bands at Seconds Granularity in Power BI and SSAS Tabular” →
Business Intelligence - BI, Data Visualisation, Power BI, Power BI Desktop, Power BI Service, Self-Service BI

Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

I was working on a project a wee bit ago that the customer had conditional formatting requirement on a Column Chart.
They wanted to format the columns in the chart conditionally based on the average value based on the level of hierarchy you are at.
Here is the scenario, I have a Calendar hierarchy as below:

  • Calendar Hierarchy:
    • Year
    • Semester
    • Quarter
    • Month
    • Day

I use “Adventure Works DW2017, Internet Sales” Excel as my source in Power BI Desktop. If I want to visualise “Total Sales” over the above “Calendar Hierarchy” I get something like this:

Line Chart in Power BI, Total Sales by Year

Now I activate “Average Line” from “Analytics” tab of the Line chart.

Adding Average Line to Line Chart in Power BI

When I drill down in the line chart the Average line shows the average of that particular hierarchy level that I am in. This is quite cool that I get the average base on the level that I’m in code free.

Power BI, Drilling Donw in Line Chart

Easy, right?

Now, the requirement is to show the above behaviour in a “Column Chart” (yes! visualising time series with column chart, that’s what the customer wants) and highlight the columns with values below average amount in Orange and leave the rest in default theme colour.

So, I need to create Measures to conditionally format the column chart. I also need to add a bit of intelligent in the measures to:

  • Detect which hierarchy level I am in
  • Calculate the average of sales for that particular hierarchy level
  • Change the colour of the columns that are below the average amount

Let’s get it done!

Detecting Hierarchy Level with ISINSCOPE() DAX Function

Microsoft introduced ISINSCOPE() DAX function in the November 2018 release of Power BI Desktop. Soon after the announcement “Kasper de Jonge” wrote a concise blogpost about it.

So I try to keep it as simple as possible. Here is how is works, the ISINSCOPE() function returns “True” when a specified column is in a level of a hierarchy. As stated earlier, we have a “Calendar Hierarchy” including the following 5 levels:

  • Year
  • Semester
  • Quarter
  • Month
  • Day

So, to determine if we are in each of the above hierarchy levels we just need to create DAX measures like below:

ISINSCOPE Year		=	ISINSCOPE('Date'[Year])
ISINSCOPE Semester	=	ISINSCOPE('Date'[Semester])
ISINSCOPE Quarter	=	ISINSCOPE('Date'[Quarter])
ISINSCOPE Month		=	ISINSCOPE('Date'[Month])
ISINSCOPE Day		=	ISINSCOPE('Date'[Day])

Now let’s do an easy experiment.

  • Put a Matrix on the canvas
  • Put the “Calendar Hierarchy” to “Rows”
  • Put the above measures in “Values”
Detecting Year, Semester, Quarter, Month and Day hierarchy levels with ISINSCOPE in Power BI Desktop

As you see the “ISINSCOPE Year” shows “True” for the “Year” level. Let’s expand to the to the next level and see how the other measures work:

Continue reading “Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI” →
Azure, Azure Analysis Services, BISM Tabular, Business Intelligence - BI, DAX, Power BI, Power BI Desktop, Power BI Service, Quick Tips, Self-Service BI, SQL Server, SQL Server Analysis Services - SSAS, SQL Server Management Studio - SSMS

Quick Tips: Export Power BI Desktop and Power BI Service Model Data In One-Shot with DAX Studio

Exporting Model Data to CSV or SQL Server in One Shot

Update 2021 March:

You can now export the data direct from Power BI Desktop using my tool, Power BI Exporter. Read more here.

In some of my old posts, which are the most popular ones, I explained how to Export data Power BI Desktop or Power BI Service data to different destinations like CSV, Excel and SQL Server. In this quick tip I explain a very easy way to export the model data as a whole to either CSV or SQL Server with DAX Studio.

Daniil from XXL BI well explained this method, but I’d rather quickly explain how it works and add some more information.

After release 2.8 of DAX Studio, you can now quickly export the whole model to CSV and SQL Server in one shot.

Enabling Export All Data in DAX Studio

  • Open DAX Studio
  • Click “File”
  • Click “Options”
  • Click “Advanced”
  • Tick “Show Export All Data button”
DAX Studio Export Power BI Model Data Settings

Export Power BI Model Data to CSV

DAX Studio Export Power BI Model Data to CSV
Continue reading “Quick Tips: Export Power BI Desktop and Power BI Service Model Data In One-Shot with DAX Studio” →
Azure, Azure Analysis Services, BISM Multidimensional, BISM Tabular, Business Intelligence - BI, On-premises Data Gateway Enterprise Mode, Power BI, Power BI Desktop, Power BI Service, SQL Server, SQL Server Analysis Services - SSAS

Preparing On-premises Data Gateway Implementation Plans for Enterprises

Definitive Guide to On-premises Data Gateway Implementation
Photo credit: Kayla Duhon

If you are a Business Intelligence consultant working on Power Platform, Azure Logic Apps and Azure Analysis Services landscape, you probably know that On-premises Data Gateway cab be one of the most essential parts of your engagements with your customers. In many cases, installing On-premises Data Gateway can be a one-man-band job but in many others, it requires teamwork effort. Either way, it can go smoothly if you already have a well-thought implementation plan otherwise, it can quickly turn into a beast that can exhaust the whole implementation team and the customer for some days.

In this post, I do my best to provide you with some guidelines that can help you with your On-premises Data Gateway implementation planning. This post may look rather long, and some of the points are generic, but it is worthwhile mentioning them. Consider the following points before, during and after the engagement:

  • Understanding the use cases
  • Culture of the engagement
  • Environments (Dev, UAT, Prod)
  • Communication
  • Security
    • Corporate/environmental firewalls
    • Proxy Servers
    • Identity Access Management
  • People
  • Documentation
  • Installation, configuration, and testing

Here is a diagram of the important points that you should consider:

Implementing On-premises Data Gateway
Implementing On-premises Data Gateway

Use cases

You need to understand the use cases of On-premises Data Gateway (Standard Gateway) for your customer. If they need the gateway for their Power Platform, Azure Logic Apps, Azure Analysis Services or all of them. This is important as you either need to have access to your customer’s Power BI Service or Azure Portal or both, or you need to assist your customer to configure On-premises Data Gateway in Azure or in Power BI Service. The next points are:

  • Accessing customer’s Azure Portal and/or Power BI Service: The customer to decide whether to create a new account with sufficient rights for you or give you the credentials of an existing account. It is important to make sure you can access all environments and you have necessary rights to install/configure the gateway
  • You assist/consult a person at customer side with the implementation: you need to make sure you communicate with that person and see if he/she understands the requirements before the implementation date. Send them a calendar invitation beforehand to make sure he/she is present at that date. Always ask for a backup person just in case of an emergency happening to the primary person.
Continue reading “Preparing On-premises Data Gateway Implementation Plans for Enterprises” →