A Fine Slice Of SQL Server
This 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.
Comments closedMarco 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.
Comments closedKurt 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.
Comments closedThis 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.
Comments closedReitse 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.
Comments closedRuben 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.
Comments closedChris 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.
Comments closedAndy 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.
Comments closedTeo Lachev takes a look at a new preview:
The June release of Power BI Desktop includes a preview of a new Power BI slicer configuration – Date Picker. It’s meant to solve two issues with report design.
Read on to see what those two issues are and how this new date picker can resolve them. It’s still in preview, so you’d have to change the settings in Power BI Desktop. And I imagine it won’t be available in Power BI Report Server because those people (including me) can’t be trusted to have nice things.
Comments closedIvan Palomaras Carrascosa groups things together:
In this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data.
Topics we will cover include:
- How to generate text embeddings for raw documents using a pre-trained sentence-transformers model.
- How to reduce the dimensionality of those embeddings with UMAP to prepare them for clustering.
- How to apply HDBSCAN to automatically discover topic clusters and visualize the results.
This is a pretty neat trick that takes advantage of the embedding model’s ability to convert raw text into hundreds (or thousands) of floating point numbers while maintaining enough of the context to differentiate ideas. A lot of it is the original word2vec concepts but scaled up.
Comments closed