A Fine Slice Of SQL Server
Andy Brownsword’s appeal is becoming more selective:
When developing Power BI models, we don’t always want to refresh the entire dataset before we start working with the data. We can speed up the refresh – and therefore our development – by reducing the volume we’re working with.
Here we’ll look at a couple of ways to use parameters to adapt production-ready models to reduce and tailor data volumes for development.
Click through to see how.
Leave a CommentEugene Meidinger opens up the command line interface:
The Tabular Editor CLI (TE CLI) is a command-line interface that makes it possible to view, query, build, manage, and test semantic models programmatically. This is different from Tabular Editor 2 or 3, which have a graphical user interface (GUI) and buttons that you click to take actions and make changes. In the CLI, you type and submit
te <command>like:
te connectto connect to a modelte lsto list its contentste vertipaqto run the VertiPaq Analyzerte add Sales/Revenue -t Measure -i "SUM('Sales'[Amount])" --saveto add a measureYou can also execute these commands in a pre-written script. This typically happens in the terminal. A terminal can be a standalone application that you open (like the default Windows or Mac terminals) or embedded in an application like VS Code or Cursor where you can see files in an explorer:
Click through for more info on how it works. Tabular Editor CLI will be free for another two months (as of the time of this post), so it’s a good time to see if this makes sense for your use cases.
Leave a CommentThis week, in the announcement about support for Fabric Pipelines in Workspace Monitoring, I noticed that it came with an Operations Agent that actively monitors and analyses Pipeline activity. And that got me thinking, since Workspace Monitoring also contains Power BI activity data, why not create an Operations Agent to actively monitor Power BI too?
Read on for a fairly simple demonstration of how it works.
Leave a CommentMarco Russo and Alberto Ferrari format things differently:
A challenging requirement in Power BI reports is that of applying different formatting rules based on the level of aggregation. At the year level, the background shade may reflect each year’s share of the grand total. At the quarter level, a status color may indicate whether the quarter is above or below the average. At the month level, the color may flag exceptional values, like months that contribute more than a defined threshold to their year. Each level has its own logic; what the conditional expression of the measure needs to know is which level the current cell belongs to.
Read on to see how you can pull this off.
Leave a CommentKurt Buhler shares some thoughts:
In a Power BI semantic model you can set freeform text fields for each object (like tables, columns, and measures) to describe what they do, how to use them, or other information. These descriptions are a convenient and structured way to document each object for developers. They’re also helpful for users, since the descriptions (unlike DAX expressions) show when you hover on the object in Power BI Desktop:
Click through to see what Kurt recommends in terms of items that should go into a description, as well as things that do not belong there. Kurt also has some strong ideas around AI-generated descriptions and what makes a description relatively more useful for a person versus a language model.
Leave a CommentThis is a blog post I’ve been meaning to write for a long time. Since Fabric launched there has been a lot of focus on Direct Lake mode in Power BI and a lot of people used it because it was the cool new thing. Arguably, we at Microsoft have been guilty of telling people to use it because it was the cool new thing without properly explaining what the benefits are of using it. Direct Lake doesn’t completely replace other storage modes: in a recent post I talked about when Import/DirectQuery composite models are the best choice; Marco wrote a good article on Direct Lake vs Import mode which makes the case for the continuing relevance of Import mode for many scenarios. So what are the main benefits of using Direct Lake mode?
Click through for Chris’s answer.
Leave a CommentReitse Eskens checks the logs:
As you’ve probably heard and read before, monitoring your Fabric environment as a whole is quite important. It really does help to know what’s going on.
Now, one thing I’ve learned over all these years is that report users do quite like their data to be as fresh and up to date as possible. And, when the data seems stale, they tend to ask questions.
Read on for some notes covering how to refresh a semantic model, when you might want to, how to automate it, and how to monitor the refresh process.
Leave a CommentRuben Van de Voorde announces a new update:
Since announcing the Tabular Editor CLI, we’ve been hard at work polishing the CLI and bashing the bugs we found, thanks to your help. We deeply appreciate all the input we received so far through GitHub, talking to you at events, comments on these blogs, and all other channels you engage with us (leave yours at the bottom of this page). Keep it coming!
We’re now at a point where we feel ready to share the updated version with you: version 0.6.0.
This is still in a limited public preview, so it’s free until the end of September. After that point, it becomes a paid product.
Leave a CommentChris Webb diagnoses a case of passive voice:
A few months ago I wrote about a rare error – the “Maximum allowable memory allocation” error – that may occur when the physical machine, or node, that a semantic model is running on in the Power BI Service comes under memory pressure. Recently, someone I was working with who was doing some load testing showed me a related error:
The operation was canceled and the database was evicted to balance the CPU load on the node. Please try again later.
Read on to see what causes this.
Leave a CommentAndy Brownsword explains one performance improvement technique for Power BI data transformations:
As a database developer, when I started using Power BI, I was concerned about it retrieving reams of data only to perform transformations downstream. The Power Query editor misleads us into thinking the retrieval and transformations are applied sequentially.
Thanks to Query Folding, that’s not usually the case. And that gives us more power to extract performance from the database.
This only works in situations where there’s something downstream to perform that processing, like a relational database. And one of the areas where you can affect performance, either positively or negatively, is in organizing operations such that you have a stretch of foldable operations. That way, all of it can run as one operation in the database.
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