Thin Reports, What Are They, Why Should I Care and How Can I Create Them?

Thin Reports in Power BI

Shared Datasets have been around for quite a while now. In June 2019, Microsoft announced a new feature called Shared and Certified Datasets with the mindset of supporting enterprise-grade BI within the Power BI ecosystem. In essence, the shared dataset feature allows organisations to have a single source of truth across the organisation serving many reports.

A Thin Report is a report that connects to an existing dataset on Power BI Service using the Connect Live connectivity mode. So, we basically have multiple reports connected to a single dataset. Now that we know what a thin report is, let’s see why it is best practice to follow this approach.

Prior to the Shared and Certified Datasets announcement, we used to create separate reports in Power BI Desktop and publish those reports into Power BI Service. This approach had many disadvantages, such as:

  • Having many disparate islands of data instead of a single source of truth.
  • Consuming more storage on Power BI Service by having repetitive table across many datasets
  • Reducing collaboration between data modellers and report creators (contributors) as Power BI Desktop is not a multi-user application.
  • The reports were strictly connected to the underlying dataset so it is so hard, if not totally impossible, to decouple a report from a dataset and connect it to a different dataset. This was pretty restrictive for the developers to follow the Dev/Test/Prod approach.
  • If we had a fairly large report with many pages, say more than 20 pages, then again, it was almost impossible to break the report down into some smaller and more business-centric reports.
  • Putting too much load on the data sources connected to many disparate datasets. The situation gets even worst when we schedule multiple refreshes a day. In some cases the data refresh process put exclusive locks on the the source system that can potentially cause many issues down the road.
  • Having many datasets and reports made it harder and more expensive to maintain the solution.

In my previous blog, I explained the different components of a Business Intelligence solution and how they map to the Power BI ecosystem. In that post, I mentioned that the Power BI Service Datasets map to a Semantic Layer in a Business Intelligence solution. So, when we create a Power BI report with Power BI Desktop and publish the report to the Power BI Service, we create a semantic layer with a report connected to it altogether. By creating many disparate reports in Power BI Desktop and publishing them to the Power BI Service, we are indeed creating many semantic layers with many repeated tables on top of our data which does not make much sense.

On the other hand, having some shared datasets with many connected thin reports makes a lot of sense. This approach covers all the disadvantages of the previous development method; in addition, it decreases the confusion for report writers around the datasets they are connecting to, it helps with storage management in Power BI Service, and it is easier to comply with security and privacy concerns.

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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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Data Visualisation with Power BI Desktop

As most of you guys know Power BI Desktop is released. I should say, it’s awesome. There are heaps of changes in compare with its preview edition Power BI Designer. I’ve written a series of posts regarding creating a report and dashboard using Power BI Designer before. You can find them here. Now I want to explain the same thing in Power BI Desktop. I’ll cover lots of new features in this post and I hope you enjoy it.

Get Data

  • Open Power BI Desktop
  • Click on Get Data. You can also get data from recent data sources or even open a predefined report stored in pbix format

Power BI Desktop 01

  • We use Adventure Works DW 2012 database as sample, you can open your real world data source
  • Click on “SQL Server Database” then “Connect”
  • In this sample we are connecting to a “SQL Server Database”
  • Click “Connect”

Power BI Desktop 02

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