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Category: Analysis Services

WaitTime in Power BI

Chris Webb explains what a new metric means:

What does WaitTime represent? Here’s the technical explanation: it’s the wait time on the query thread pool in the Analysis Services engine before the query starts to run. But what does this mean for you as someone trying to tune DAX queries in Power BI?

Chris provides an examplation of exactly that. This tracking of noisy neighbors is interesting, as it would provide insight if you’re noticing variance in dataset refresh times.

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Fixing VertiPaq Analyzer Dictionary Size Errors

Marco Russo troubleshoots an issue:

There are cases where the dictionary size reported by VertiPaq Analyzer (used by DAX Studio, Bravo for Power BI, and Tabular Editor 3) does not correspond to the actual memory required by the dictionary. However, the number reported is technically correct because it represents the memory currently allocated for the dictionary. The issue is that – after a refresh – this memory amount is larger than the actual memory required for hash-encoded columns.

Read on to learn what the consequences are and how you can resolve this in Power BI Desktop as well as in Analysis Services.

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Thoughts on a Migration: Azure Analysis Services to Power BI Premium

Dan English shares some thoughts:

Over the past month couple of months I got the opportunity to test out the new migration experience that was just made available for Public Preview this past month during the PASS Data Community Summit and announced on the Power BI blog here Accelerate your migration experience from Azure Analysis Services to Power BI Premium with the automated migration tool. The blog post also shows a very quick animated gif walkthrough of the process and there is a thirteen minute video from the MS Build conference earlier this year where this was first demoed that you can check out here as well The Future of Enterprise Semantic Models.

Click through for a detailed analysis.

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What’s New in SSAS 2022

Chris Webb goes digging:

There was a time when a new release of SQL Server – and therefore a new release of SQL Server Analysis Services – was the most exciting thing in the world for me. New functionality! New things to blog about! Not so now that my focus, and Microsoft’s, is on Power BI and we get cool new functionality there every month. All the same there are still a lot of people running SSAS on-premises and SQL Server 2022 has just been released, so what’s new and is it worth upgrading?

Read on for Chris’s thoughts. MDX even makes an appearance.

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Migrating Azure Analysis Services to Power BI Premium

Gilbert Quevauvilliers dumps AAS:

I thought it would be a good idea to walk through the steps when looking to migrate AAS to PBI.

In the past when I had to do this for clients it was a lot of manual steps and a lot of small things to get just right. This process is now seamless and awesome!

Reviewing Gilbert’s step-by-step process, yeah, this is easy, though watch out for the pitfalls Gilbert found.

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Tracking Power BI Desktop Activity in SQL Server

Chris Webb looks in on things:

Something I do all the time when performance tuning Power BI is use SQL Server Profiler to monitor query and refresh activity. There’s a handy external tool that lets you open up Profiler with a connection to Power BI Desktop; if you’re using Power BI Premium you can also connect Profiler up to a published dataset using the XMLA Endpoint. Profiler is a bit old-school though and likely to lead to snarky comments from SQL Server DBAs (it’s not deprecated for Analysis Services though!) who’ll say you should be using Extended Events (aka xEvents) instead. And guess what – did you know you can use Analysis Services xEvents to monitor activity in Power BI Desktop?

It’s the power of Extended Events.

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Releasing a Tabular Model without Users or Roles

Olivier van Streenlandt hit a deployment problem:

A couple of weeks ago my team & I ran into an issue with SQL Server Analysis Services (SSAS), due to a network split between companies, We weren’t able anymore to manage our SSAS access into our SSAS Tabular Model. Since deploying a Tabular Model using Visual Studio is also overwriting members & roles, we needed to find a valid alternative to execute our deployments. Manually at first and automated in the end.

Read on to see how they used Azure DevOps pipelines to solve the issue.

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Power Automate and Dataset-Driven Power BI Subscriptions

Dan English follows up on a prior topic:

In the last post I went over using Power Automate to perform a data driven report subscription using a Paginated report referencing an AAS database. The flow referenced an Excel file with the information to make the process data driven and generate 2000 PDF files that could then be emailed to users. In the flow the PDF files were simply placed in a OneDrive folder for testing purposes to validate the flow would run as expected and to review the metrics after the fact to evaluate the impact of running the process.

For the follow up there were two items that I wanted to compare against the original flow

1. Moving the AAS database being referenced to a Power BI dataset hosted in the same capacity as the Paginated report

2. Using a Power BI report instead of a Paginated report

In this post I will cover the first comparison. 

Check out what changes and what stays the same between using Azure Analysis Services and Power BI-hosted datasets.

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Object-Level Security in Power BI and Analysis Services

Marco Russo and Alberto Ferrari take advantage of object-level security:

To be more technical, object-level security can be applied to tables and columns, but it cannot be applied to measures directly. If a measure – whether in a direct or indirect way – references a column or a table that is not accessible under the current security context, the measure becomes invisible as well. This way, it is guaranteed that if a column must be invisible to a group of users, its content cannot be inferred by looking at the result of measures based on said column.

Concretely, there may be cases where a measure should be hidden from a group of users without removing the visibility of existing data structures. Let us look at a simple example first. We define the Sales Amount measure as the product of Sales[Quantity] by Sales[Price]. You also have a Discounted Sales measure that applies a set discount Sales Amount; now how can you hide Discounted Sales from a group of users without hiding the initial Sales Amount measure? By hiding either Sales[Quantity] or Sales[Price], you would hide both measures. Because the discount is set inside the Discounted Sales measure and not stored in the model, it looks as though you cannot hide just the measure. However, it we create a dependency in Discounted Sales on an empty hidden table specifically created to generate that dependency, we can hide Discounted Sales by hiding that table.

Read on to see how.

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Making the Case for Azure Analysis Services

Teo Lachev makes the case:

Microsoft BI practitioners have three options for hosting semantic models: SSAS (on prem), Azure Analysis Services (cloud), and Power BI (cloud). AAS is somewhat caught between a rock and a hard place. Given that Power BI gets the most attention for cloud deployment, why would you consider AAS at all? There are two main reasons:

Read on for the reasons. Knowing how much it does cost, it almost feels like trying to thread a needle: if you don’t spent enough or have enough data, Power BI is typically much more efficient; if you have sufficient data, I’d want to do a proper cost analysis between on-prem (or IaaS) Analysis Services and Azure Analysis Services.

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