Quick Tips: Time Dimension with Granularity of Seconds in Power BI and Analysis Services Tabular Models

Quick Tips: Time Dimension with Granularity of Seconds in Power BI and Analysis Services Tabular Models
Photo by Markus Spiske

In an article I posted a while back I showed different methods of creating Time dimension in Power BI and Tabular models. The Time dimension I explained was in Minutes. In this post I show you simple way to create Time dimension supporting Seconds. As this is a quick tip, I only show you how to get the Time and ID columns in the Time dimension. If you need to add time bands (time buckets) check this out for more details.

Time Dimension in Seconds Grain with Power Query (M):

Copy/paste the code below in 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]))),
#"Changed Type" = Table.TransformColumnTypes(#"Time Column Added",{{"ID", Int64.Type}, {"Time", type time}})
in
#"Changed Type" 
Time Dimension in Power Query

Time Dimension in Seconds Grain with DAX:

Run the DAX expression below in a new calculated Table in Power BI or SSAS Tabular model:

Time in DAX = ADDCOLUMNS(
                       GENERATESERIES(1, 86400, 1)
                        , "Time", TIME(0, 0, 0) + [Value]/86400
                        )
Time Dimension in Seconds Level with DAX
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Time Dimension in Power BI and SSAS Tabular Model Supporting Minutes Time Bands

2018-05-23 12_58_48-Symbols (Open in Visio).vsdx - Visio Professional

Date dimension has been discussed quite a lot on the Internet and you can find lots of valuable articles around it here and there. But what if you need to analyse your data in time level? A customer has a requirement to analyse their data in Minutes level. This means that the granularity of the fact table would be at minute level. So, if they store the data in their transactional database in seconds level, then we need to aggregate that data to minutes level. I don’t want to go there, just bear in mind that the granularity of your fact table is something that you must think about at the very first steps. In most cases, if not all cases, you’d be better to have a separate Time dimension. Then you need to have a TimeID or Time column in your fact table to be able to create a relationship between the Time dimension and the fact table. In this post I show you two ways to create Time dimension in Power BI:

  • Creating Time dimension with DAX
  • Creating Time dimension with Power Query (M)

Alternatively, you can take care of the Time dimension in the source system like SQL Server. Continue reading and you’ll find a T-SQL codes as complementary.

The techniques that I explain here can be done in SSAS Tabular model and Azure Analysis Services as well.

Requirements:

To follow the steps of building the test model you need to have:

  • Power BI Desktop: Download the latest version from here
  • A sample fact table containing time or datetime. I modified FactInternetSales from AdventureWorksDW and made it available for you to download in Excel format (find the download link at the bottom of the post)

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Side-by-side Role-Playing Dimensions In Power BI

Role-playing dimension is one those concepts that is discussed a lot from time to time. I also posted an article about implementing role-playing dimensions in Tabular models.

To recap, in the role playing dimensions in SSAS Tabular article I explained three different solutions:

  1. Importing role playing dimensions several times into the model
  2. Creating database views in the source side (in case your source is a from of RDBMS like SQL Server, Oracle etc…) then import the data into the model
  3. Keep the inactive relationships in the model and create several measures to take care of different roles using USERELATIONSHIP functions in DAX

I this post I explain implementation of the third option above. In this scenario you need to create especial calculated measures based on the roles you have in a fact table. One the most common role-playing dimensions is Date dimension. Consider you have to show Internet Sales Amount by Order Date, Due Date and Ship Date in a single chart in your report. In this case, having 3 different date tables won’t help us to achieve the goal.

New to Power BI? Quickly learn about Data Visualisation in Power BI here.

Defining new Measures in Power BI Desktop

Basically, what I’m going to explain in this post is using inactive relationships between FactInternetSales table and the DimDate dimension by adding a new Calculated measure. In this case, we’ll be able to show Sales Amount by different roles, well, dates in this sample in a single chart.

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Role-playing Dimension in SSAS Tabular Models

First of all I’d like to explain what a Role-playing dimension actually means. Then I’ll express the way you can implement it in a SSAS tabular model.

When you link a dimension to a fact table several times for logically distinctive roles you’re using a role-playing dimension.

The key points are:

1.       You are linking a fact table to a dimension multiple times. The relationships are defined by linking multiple foreign keys in the fact table to a single key in the dimension table.

2.       Each linkage represents a single role or concept

The most popular role-playing dimensions are DimDate and DimTime. Do you want to see how to implement Role Playing Dimensions in Power BI, Click here and here.

NOTE: The sample is from Microsoft “AdventureWorksDW” for SQL Server 2012 and might be different from your own data warehouse design.

For instance, in a sales system that you have something like FactInternetSales fact table which has several links, or relationships, to a DimDate or DimAddress for distinct concepts like “Order Date”, “Ship Date” and “Due Date”.

As you see, all of the above columns obviously represent different meanings of date. In the data warehouse design you’ll see something like this:

role-playing dimension 01

Although this is absolutely OK in the relational database layer, but, this sort of relationship is NOT permitted in the tabular model, so what should we do?

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Creating a Simple Date Dimension Using Recursive Common Table Expressions (CTE)

In this post I’ll explain how to create a simple date dimension to use it in your data warehouses and your BI solutions. So, this article is for you if you need a fast and easy way to make a simple date dimension that supports the most commonly used date elements like

·         Integer date key

·         Different date formats

·         Quarter

·         Month names

·         Week numbers

·         Day of the week

·         Day of the year

·         Is day end of month

·         Not available (N/A) row

Due to the fact that there are lots of fellows that are still using SQL Server 2008 and earlier, I put the codes that support SQL Server 2008  as well as SQL Server 2012. But, I’ve commented the 2008 lines.

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Digging into SQL Server 2012 columnstore index

The SQL Server 11.0 release (code named “Denali”) introduces a new data warehouse query acceleration feature based on a new type of index called the columnstore. Columnstore indexing is officially announced in SQL Server 2012. It is working based on xVelocity memory optimised technology and it improves data warehouse query performance significantly. Due to the fact that data warehousing, decision support systems and business intelligence applications are growing very quickly, we need to be able to read and process very large data sets quickly and accurately into useful information and knowledge. Columnstore index technology is especially appropriate for data warehousing data sets. It improves the common data warehousing queries’ performance significantly.

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