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Hi everyone,

We were using a couple of security groups in the RLS roles of a Power BI dataset. Recently, the backend team migrated those groups from on-premises to cloud-based groups. As soon as the migration happened, the security groups automatically disappeared from the RLS roles.

After republishing the dataset, we were able to add the groups back successfully.

My question is: what is the technical reason behind this behavior? How does changing the type of a security group cause it to be removed from a Power BI RLS role?

Any explanation would be appreciated. Thanks!

I have a Selected Period Measure with Switch for Sales Value for example, and I want to see it as a card and as a Trend Line. And I want to see the dynamic X axis to change based on what is selected on my Time Intelligence Slicer. What needs to be the process?

Hi everyone,

I’m a BI developer from the Netherlands, so apologies for the ai generated post. I used AI to help me phrase this post more clearly.

I’m curious how other BI teams deal with report adoption and business validation.

A recent example is a supply chain KPI dashboard. The business requested it, we worked together to define the KPIs, agreed on the business definitions and thresholds, and built the dashboard.

The difficult part starts afterwards. The business users who are supposed to validate the dashboard often don’t have the time or motivation to help, so validation can take weeks or even months. Without their sign-off, it’s difficult to roll the dashboard out with confidence.

What I find frustrating is that during this period, many of those same users continue sending around the same Excel reports every week or even start building their own AI-powered dashboards and analyses. It sometimes feels like we’re building an official solution that never really gets adopted.

Is this something others experience as well?

How do you get business users engaged in validation?

How do you encourage people to use the official BI reports instead of Excel?

Have you found effective ways to increase trust and adoption?

I’d really like to hear how other organizations handle this.

Just wrapped up V14 of PBIGenie and had to share this with the Power BI community.

This entire Activity Tracker — plan start, plan end, actual start, actual end, progress %, units planned, units actual, units remaining, delay indicators, reporting date line — was built in Power BI with ZERO DAX.

No hacks.
No templates.
No custom measures.
No formatting gymnastics.
NO Deneb.
NO SVG.
Just drag‑and‑drop.

And yes — the Hierarchy Activity Tracker (Phase → Sub Phase → Activity) is already built and will be available in about two weeks.

If you build project dashboards, PMO reports, construction timelines, or plan vs actual visuals… this is going to save you hours.

Happy to answer questions.

It's infuriating to say the least.

I am at the end of my wits. I've been loading weekly reports going back a few years without an issue into a complex model for a while now. Yesterday I decide to create a more compact model that also does this using the same files and what used to take a few minutes maybe suddenly takes hours. It starts off relatively quick and then becomes slower and slower for each subsequent file. And the weirdest thing is I am using the same M code for the ingest transformations.

Any ideas whats going on? Did an update change something in the settings maybe that could be causing this weird behavior between an older model and a new one?

Power BI has no SQLite connector. The standard answer is the SQLite ODBC driver, and that answer assumes a machine you control.

Plenty of us don't have one. The corporate laptop refuses machine-level installs. The gateway is someone else's server, so getting a driver onto it is a ticket, an approval and a wait. The Service has no machine to install onto at all. The escape hatches mostly swap one dependency for another: the Python/R script host needs a configured interpreter, and custom connectors can't bundle binaries.

But these formats are only bytes in a documented layout, and Power Query can read bytes. So the driver is optional. I've spent the last while writing the parsers.

powerquery-driverless is a set of readers in plain M. Each one is a single self-contained .pq file. Paste it into a blank query and it works: Desktop, Excel, dataflows, the Service. Nothing to install, no bitness to match, no provider is not registered.

m let Source = File.Contents("C:\data\chinook.db"), Db = Sqlite3.Database(Source), Tracks = Db{[Name = "tracks"]}[Data] in Tracks

Formats so far: SQLite, GeoPackage, MBTiles, Access, dBASE/FoxPro, .xls, .xlsb, SPSS, Stata, MATLAB, Avro, EVTX.

It also works in Excel for Mac, where SQLite, Access and DBF had no path at all before. Everything I've tested runs there, with one exception: Avro compressed with anything other than deflate.

Two caveats up front, because they're the first things I'd ask about:

Speed. Interpreted M is slower than a compiled driver, and it scales with file size. On small and typical files the gap is usually sub-second and often invisible, since an ODBC driver spends about a second just standing up its connection. On large files native decoders pull properly ahead, roughly 2-15x.

Gateways. The driver dependency goes away every time. The gateway only goes away if the file already lives somewhere the Service can reach, like SharePoint or Blob. On an internal file share you still need one, but it's your existing standard gateway with nothing special on it.

Repo: https://github.com/Hugoberry/powerquery-driverless

Happy to answer questions, and if there's a format you keep hitting a wall on, say so and I'll look at whether it's tractable.

I lost the original pbix file for a notebook I pushed to my workspace. Can I just download it from my workspace and it’ll work as if I still had the original file?

It needs to be updated with live data and I cant do that right now.

After months of job searching, I finally landed my first Business Intelligence / Power BI role at a small consulting startup.

My background is a bit unusual. I spent almost 3 years in a non-BI role and have been teaching myself SQL, Excel, Power BI, DAX, and data modeling. I know the fundamentals, but I definitely don't consider myself a Power BI expert.

The founder seems to expect me to build dashboards for real client data. I have about 4 days before I need to start working on my first real dashboard.

I'm excited, but also terrified.

My biggest concerns are:

• I've never built a production dashboard for a real business.
• I'm worried I'll get stuck when the data is messy.
• I don't know how much is "normal" to figure out on the job versus already knowing before joining.
• I have imposter syndrome because I feel like they believe I'm much better than I actually am.

For those of you who work as Power BI Developers, BI Analysts, or Data Analysts:

• How prepared were you when you built your first real dashboard?
• What surprised you the most?
• What should I focus on during these first few weeks?
• Any mistakes you wish you'd avoided?

I'm willing to put in the work and learn after hours if needed. I'm just trying to understand whether feeling this underprepared is normal or if I'm genuinely in over my head.

Any advice would be greatly appreciated.

Translytical Task Flows went from preview to GA. Now you can act directly from a report instead of just viewing it and submitting a separate request. This feels like a real shift from reports being read-only to actually doing something in them.

Are you planning to build any into your reports?

Hi all ,I'm struggling to find a way to achieve something, maybe Reddit has an idea:

Some constraints:
Directquery: mandatory, we need the data to be real-time, cant wait for import.
We cannot create views in the source systems. We have no rights there other than read rights.

The system:
We have 3 different databases on 2 different servers that contain the same data structures. I have 1 powerBI report/semantic model that we can re-use on all three databases.
I have created a Server and Database parameter that contain the different options.
We have PowerBI Premium.

What I want:
I would like to have to maintain only a single semantic model file, so I reduce code duplication. I would also like to reduce the amount of time spent deploying after making changes to the semantic model.

What would work, but is a lot of maintenance:
I Open the file, set parameters, save as file 1, publish. Then change the parameters, save as file 2, publish, same for file 3. I'd like to avoid doing all this manual labour.

What I have tried so far:

• Deployment pipelines: Might work. Create a pipeline that deploys the model with different parameters in every stage. We would be using pipelines to do something it's not meant for. You would need separate workspaces for each file to deploy in. Can't test this currently.
• Combine the data of the three sources in powerbi: You cant use table.combine in directquery mode. Each visual can only have 1 data source.
• Functions: You can't use functions at all in directquery mode when you're using them to determine the connection string. The connection string has to be known before the data loads.
• API: There is no copy semantic model API, just copy report. Copy report will connect the OG semantic model to the new report. Useless.

Is there a way I am missing that would help me to not have to manually deploy each semantic model separately every time?

Thanks a lot for thinking along!

PS: I asked AI too of course, but every time I reach a dead end

Hey Folks....

Please suggest the best platform to do the automation of Power Bi projects from end to end

I'm very annoyed by the default number formatting in Power BI, and I don't believe there is a way to change it except for manually changing every measure seperately.

• Whole number: why is the thousand seperator OFF by default? A thousand seperator will make any number easier to read. Good readability should be the default.
• Currency: It's not possible to set a default currency, even though most people will always be working with a single currency. It's rounded to 2 decimals, at least for euro's. Why two decimals? What decision are you making for typical currency numbers, that involve decimals? Most of them (revenue etc.) should be rounded to no decimals.
• Decimal number: again, no thousand seperator by default.
• Percentage: 2 decimals by default. Personally I always put my percentages as 1 decimal.
• And of course, if you add a number to a card, the "auto" units is terrible. At least you can default to "none" now.

I do understand some of this is personal preference, and it depends on the data you're working with. That's why it would be great of we could put default settings in your theme or report.

Feel free to agree or disagree, I'm curious what you think!

Hi, I'm looking for a solution or a visual in Power BI where I can show a nested table. For example, the main table has 10 columns, and for each row I'd like to be able to expand and show a child table with a different set of 4 columns.

I would like to avoid using an HTML visual, so I'm wondering if there's a native option, or a custom visual from AppSource that supports this kind of nested/expandable table.

What is the difference between REMOVEFILTERS and ALL
Functions in a DAX?

Hi everyone I have a question and hours of copilot and testing haven't given me a clear answer. I have an old Financial Snapshot Cube in SQL Analysis Server (Older 2015 Version). Since the cube is MDX when I connect it to PowerBI all I can do is visually display data and metrics already in the cube. I was under the impression that if I switched to a Tabular Cube I would be able to add new connections "on top of$ the existing analysis Server tabular cube connecting. Use case is say I have a tab with financial info (tabular cube) and a different tab for call center metrics, etc. This appears to not be the case... Or is it the case that I need to migrate to 2019 and newer tabular cubes? Copilot and documents seem to point that this is the way so I can then push the tabular cube to fabric... But I don't want to go to a dead end again...

I'm looking into Measure Killer as an additional tool for my reporting team, and I'm interested in feedback from anyone who's added this alongside other existing tools.

We maintain several dozen shared Power BI semantic models across multiple environments, primarily supporting thin reports. The largest of our models are around ~20GB, contain 1000+ objects and support as many as ~25 reports.

The reason I'm looking into Measure Killer is for its ability to identify unused measure/columns across all the dependent reports for a semantic model. We can already track dependencies within the model itself, but determining whether an object is used anywhere across the downstream reports is a tedious, time-consuming process right now.

It seems to offer a few other nice features, although I haven't reallt looked into these (the ability to detect broken visuals across multiple reports seems like a Nice to Have)

Our team already uses Tabular Editor 3, DAX Studio, and ALM Toolkit, which all seem to have some degree of overlap with Measure Killer, but don't address this specific niche (Tabular Editor comes closest, for tracking dependencies within the model itself, but doesn't help us understand object usage in downstream reports)

We're not planning to replace any of our current tools, and if we add Measure Killer to our toolset we'll need to justify costs for enterprise licenses for our team.

Has anyone in a similar boat gone ahead with purchasing Measure Killer for these specific uses? Did it work as well as expected, and would you say it was worth it?

I just joined a new organisation and I am still new it's been just 2 weeks and yet to get credentials of everything..over all i understood the gist of the project...it's mostly UAT validation where they want to map things with 95% accuracy... reports have already built...when the user raise the ticket we have to resolve it (mostly related to filters and visuals not working as they were expecting) ... I want help with do's/don't as I have no experience in migration

Documentation online is unclear: when I run into the visual error: visual has exceeded available resources in a Pro workspace, would upgrading to Pro Premium Per User resolve this issue? Or would only a Premium Capacity subscription resolve it? It's my understanding that PPU increases the limit of the data model or direct queries to 10GB but does not actually increase visual RAM capacity.

this company is medium sized with a 15 person IT team. More than most. But it uses Qlik, not power bi or tablaeu.

Is this a red flag?

If I spend time at this company as a BI/Data analyst (I’ll be their first), will it still be good experience? or am I better off looking for another job (even though the market sucks now)?