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Category: Power BI

Power BI Aggregation Precedence

Published 2019-12-05 by Kevin Feasel

Shabnam Watson asks and answers a question of importance:

Precedence is one of the aggregation properties that you can define on an aggregation table in Power BI. In a model with multiple aggregation tables, you can use Precedence to define the order in which aggregation tables will be considered by Power BI to answer queries. The higher the Precedence number, the sooner the aggregation table will be considered. Very simple and easy to understand. but the question is:

What does Power BI do when there are multiple aggregation tables configured with the same Precedence value and they can all answer the same query? Which one is considered first? Does it choose the smallest one? or is there another rule in place?

Click through to find out.

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Testing In-Browser Power BI Report Performance

Published 2019-12-04 by Kevin Feasel

Chris Webb gives us some tips on testing Power BI reports in a web browser:

It turns out that testing performance of a report in the browser is not as straightforward as it seems. In this post I’m going to describe some of the factors you have to take into account when doing this type of testing; in the next post I’ll go into more detail about how you actually measure report rendering times in the browser and how to see what happens when the report is rendered.

Click through for those factors.

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Using PowerPoint to Create Power BI Layouts

Published 2019-12-03 by Kevin Feasel

Jon Fletcher has a good tip for snazzing up a Power BI dashboard:

First question, why bother with layouts?
Using layouts in Power BI allows a user to make their visuals stand out better, the page looks professional and more appealing to its audience.

Second question, why PowerPoint?
The default page size in Power BI desktop is 16:9, (this trick doesn’t work for other Power BI page sizes), which is identical to a PowerPoint slide.
Therefore whatever is designed in PowerPoint will fit onto a Power BI page perfectly. Also PowerPoint is very easy to use; most people are familiar with it.

Click through for an example. It’s easy to go overboard with this, but Jon does a good job of using a muted color so that the edges don’t overwhelm your eyes. I might knock it down a shade or two further from that, but regardless, this is a nice tip.

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Shrinking Your Power BI Dataset Sizes

Published 2019-12-03 by Kevin Feasel

Gilbert Quevauvilliers wanted to reduce Power BI memory usage:

I had already applied all the best practices in terms of reducing the cardinality, removing unwanted columns and making sure that only the data required is being brought into the dataset. Even at this point the dataset size was consuming 90GB of memory in Azure Analysis Services. With the steps below I got my dataset size down to a whopping 37GB of memory!

I used the awesome tools from SQLBI.COM and DAX Studio to see which columns were consuming the most space, and because my dataset had currency converted values, this meant that the cardinality was very high. (The reason that I decided to store the currency conversion values, is when trying to do it on the fly in a large dataset it is very slow)

Two simple tricks led to a pretty nice reduction in size.

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Finding Power BI V1 Workspace Owners

Published 2019-11-25 by Kevin Feasel

Brett Powell has a process to find Power BI V1 workspace owners:

As most readers of this blog likely know, there are two very different kinds of workspaces in Power BI – V1 or ‘classic’ workspaces which are tied to Office 365 groups and V2 or ‘modern’ workspaces which are not. V2 workspaces have many advantages beyond their independence from Office 365 which you can read about elsewhere but for a bit of context you can read the GA announcement of V2 workspaces from back in April.

Since upgrading to V2 workspaces has been a manual process thus far, most Power BI tenants contain a mix of V1 and V2 workspaces. You may also have read the recent announcement of a new feature in the Power BI service available to workspace admins to upgrade their V1 workspaces. This blog post is all about identifying these V1 workspaces and their admins.

Click through for the process, as well as Brett’s recommendation regarding migration to V2 right now.

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Power BI Premium Capacity Testing

Published 2019-11-25 by Kevin Feasel

Matthew Roche announces an interesting tool:

This new tool was included as part of the BRK2046  session on Power BI Premium at MBAS. The whole session is valuable, but the tool itself comes in around the the 32 minute mark. There’s a demo at the 37 minute mark. The tool is available today on github.

This tool will help Power BI Premium customers better plan for how their specific workloads (reports, dashboards, datasets, dataflows, and patterns of access) will perform on a given Premium capacity.

Click through for instructions and a description of how it works.

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Aggregations in Power BI

Published 2019-11-22 by Kevin Feasel

Shabnam Watson takes us through aggregations in Power BI:

In Power BI, Aggregations start as tables just like any other table in a model. They can be based off a view or table in the source database, or created in Power BI with Power Query. They can be in Import or Direct Query storage mode.

Once in the model, these tables can be configured so that the engine can use them instead of a detail table to answer queries when possible. The process of creating and configuring aggregations in Power BI is significantly easier than the process of creating aggregations in SSAS multidimensional.

Once an aggregation table is configured, it becomes hidden from end users. Report developers and end users don’t know that it exists and don’t need to change anything in how they query the dataset.

This was one of the key benefits to a multidimensional model. Shabnam has an excellent, detailed article here, so give it a read if you are a Power BI developer.

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Decomposition Trees in Power BI

Published 2019-11-22 by Kevin Feasel

Tomaz Kastrun takes us through a new visual in Power BI:

Decomposition tree is a data presentation of slicing and dicing of selected metrics based on the attributes of these metrics or with combination of other metrics. Another great aspect of this visual is to analyze the selected variable with many metrics or attributes (dimensions) as the same time.

It’s not the type of visual I’d want to see on a dashboard, but I can see it as quite useful in exploratory data analysis.

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