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Category: Power BI

Visio Licensing Changes and Power BI

Published 2021-06-11 by Kevin Feasel

Chris Webb ties a new Visio announcement to Power BI:

There was an interesting announcement today regarding Visio:

https://www.microsoft.com/en-us/microsoft-365/blog/2021/06/09/bringing-visio-to-microsoft-365-diagramming-for-everyone/

In summary there will soon be a lightweight, web-based version of Visio available to anyone with a Microsoft 365 Business, Office 365 E1/E3/E5, F3, A1, A3 or A5 subscription. Previously Visio was not part of the main M365 plans and was only available as a separate purchase.

So what? As a Power BI user, why should I care? 

Read on for Chris’s answer. If the web-based version of Visio is good, I’m reasonably excited by this prospect.

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Clarifying Confusion around Power BI Goals

Published 2021-06-10 by Kevin Feasel

Treb Gatte continues a series on Power BI Goals:

Power BI Goals enables you to present the status of a key outcome that can optionally be tied to data. Treating Power BI Goals as a glorified hierarchy of metrics may lead you to miss a more valuable use value of Goals.

Note, Goals do not roll up. The hierarchy is there to provide a context for the goal and subordinate goals. If you need data rollup, you may want to look at alternatives.

Part 4 of our blog series covers the ability to support OKRs (Objectives and Key Results) with Power BI Goals. OKRs are a very powerful mechanism for remote workers to stay in sync and focused on the most important work.

Read the whole thing.

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Limitations in Power BI Aggregations

Published 2021-06-10 by Kevin Feasel

Teo Lachev looks at a couple of limitations in Power BI aggregations, as well as workarounds for those limitations:

Power BI aggregations are meant to speed up queries to large DirectQuery tables, as a DBA would create summarized tables to speed up queries to large tables. The most appealing aspect of telling Power BI about these aggregations is that Power BI will automatically redirect the query to the aggregation cache if it determines that its dimensionality matches the dimensionality of the aggregated table, as explained in the documentation. However, there are a couple of limitations worth emphasizing that will prevent this from happening:

Click through for those limitations and what Teo & co did to move forward despite them.

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Understanding SUMMARIZE in DAX

Published 2021-06-08 by Kevin Feasel

Alberto Ferrari dives into a DAX operator:

If you like to follow best practices, you can just read this paragraph out of the entire article. If you are using SUMMARIZE to calculate new columns, stop. Seriously, stop doing it. Right now. Open your existing DAX code, search for SUMMARIZE and if you find that you are using SUMMARIZE to compute new columns, add them instead by using ADDCOLUMNS.

At SQLBI we are so strong on this position that we deliberately omitted a part of the detailed description of the behavior of SUMMARIZE in our book. We understand how SUMMARIZE works but we do not want your code to return inaccurate results, just because you use a function without understanding when its result might be different from the result you expected.

Read on as Alberto explains why, as well as the details of SUMMARIZE and how easily you can find yourself in a mess with it.

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A Review of Tabular Editor 3

Published 2021-06-08 by Kevin Feasel

Matt Allington reviews a paid product:

Tabular Editor is a Power BI Tabular Modelling productivity tool developed by Daniel Otykier. I blogged about Version 2 of the Tabular Editor in this article here. The 3rd edition of Tabular Editor has just been released, and it is a major upgrade from version 2. TE 3 is not free, but in my view, the productivity benefits make it a must have piece of software for anyone that is regularly writing DAX in Power BI Desktop.

Read on for the review.

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Integrating Power BI Deployment Pipelines with Azure DevOps

Published 2021-06-07 by Kevin Feasel

Marc Lelijveld shows how you can combine Power BI deployment pipelines with Azure DevOps:

Looking at the Power BI release plan, dataflow support for Deployment Pipelines is coming up shortly! Currently it is scheduled for June 2021 to reach the public preview state. Versioning and DevOps integration go hand-in-hand to our opinion. With Azure DevOps Git integration, we can overcome the versioning challenge while integrating with Azure DevOps at the same time, as described in the previous blog in 2019. Today, we release a new version of the DevOps implementation which uses native Power BI functionality. Stay tuned!

As we really like the metadata deployment and the ease of setup a pipeline in the Power BI Service, Ton and I decided to setup an Azure DevOps extension based on the recently released Power BI REST APIs for Deployment Pipelines. Although Microsoft promised to come-up with a native DevOps extension over time, we decided to go for it. Time to bridge the gap!

Read on for more details.

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Speeding Up Power Query with Evaluation Container Memory

Published 2021-06-07 by Kevin Feasel

Chris Webb notes a new toggle in Power Query:

However if you have just read the docs you may be wondering what these two new registry key settings actually do. In this post I’m only going to talk about one, MaxEvaluationWorkingSetInMB; I’ll leave ForegroundEvaluationContainerCount for a future post.

At various times in the past I have blogged about how, when you run a Power Query query, the query itself is executed inside a separate process called an evaluation (or mashup) container and how this process has a limit on the amount of memory it can use. Some transformations such as sorting a table, doing a group by, pivoting and unpivoting require an entire table of data to be held in memory and if these operations require more memory than the evaluation container is able to use then it starts paging and query performance gets a lot worse. 

Read on to see where setting the max evaluation working set in memory can help, as well as the caveats that Chris lays out.

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Designing and Managing Large Datasets in Power BI

Published 2021-06-02 by Kevin Feasel

Paul Turley continues a series on doing Power BI the right way:

I was just talking to a consulting client about the best approach to build a data model and he told me something very interesting about the way they were loading data into Power BI. He said “We don’t use facts and dimensions, we load all of our data into one huge table.” He said that their data model performs well and that it meets their reporting needs. It is a difficult point to argue, when something is working at the time although the design might not follow the accepted rules. Life is like that and there are plenty of analogies to make the point that a practice, even a real bad practice, might solve a problem for a period of time and under certain conditions. <analogy>You can drive a car at excessive speed to get to your destination faster. You might not get caught by the police on that day and you might not crash but eventually, if you make it a habit, this practice will catch up to you.</analogy> Data is like that. If you don’t play by the rules, you limit your options. Bending the rules lets you move faster and sometimes with less hassle. But, as the project scope expands – and after adding enough data or other complexities to the solution, it will not endure. The data model won’t perform well, won’t load the correct data or it just won’t be reliable.

This post will explore the realities of best practice design for large data models; some important considerations and trade-off decisions when working with both “big data” and “large data”.

Read on for Paul’s tips.

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Comparing Azure Analysis Services Scaling to Power BI PPU

Published 2021-06-02 by Kevin Feasel

Gilbert Quevauvilliers continues a series on migrating from Azure Analysis Services to Power BI Premium Per User:

If you missed the first part of the series here is the link here: Query Performance – Part 1 Migrating Azure Analysis Services to Power BI Premium Per User – Reporting/Analytics Made easy with FourMoo and Power BI

In this blog post I am going to investigate how well does PPU scale when comparing it to AAS.

When comparing AAS to PPU, I must find the same size AAS size to what we get with PPU.

Read on for Gibert’s findings.

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