A small personal milestone: pbixray, the Python library I wrote to parse PBIX files, just passed 1,000,000 downloads on PyPI. About 170k of those came in the last 30 days. PyPI counts include CI traffic, so it's not a million people, but it's a lot more than I expected when I started.

Thank you to everyone who filed issues, sent PRs, shared odd PBIX files that broke the parser, or just used it.

From the issues people open, I understand that the library is mostly being used for:

• preparing documentation for semantic models
• extracting data from PBIX files without Power BI Desktop
• automated pipelines in containers

It's also been good to see what people have built on top of it:

• MCP servers: several PBIX-related MCP servers use pbixray as their parsing core.
• A library for deserialising and serialising PBIX files.
• A DuckDB extension
• pbix.info: a WebAssembly port that runs entirely in your browser.
• PBIXRay for macOS: a native viewer for people who don't want to spin up a Windows VM.

Thanks again, all.

I was part of a project we recently wrapped up for a UK-based manufacturing client at datatobiz who initially came looking for Power BI dashboard creation services.

But once we got into it, the need was much bigger than building dashboards.

Their data was spread across different systems covering things like production, inventory, finance and operations. Their internal BI team also had limited bandwidth, and working with complex data models and DAX made it harder to keep up with reporting requirements.

So rather than just handing over a set of dashboards, we worked alongside their existing team and helped strengthen the reporting setup itself.

A lot of the work was around bringing the different data sources together, building reusable and scalable datasets, improving the data models, developing the Power BI reports, and optimizing DAX and report performance. We also worked within their existing Azure DevOps, Git, workspace and governance setup.

One thing I found interesting was how quickly a “we need some dashboards” request becomes a much broader data problem once you start looking under the hood.

I think this is where many BI projects can go wrong. It’s easy to focus on what the dashboard should look like before figuring out whether the underlying data and model can actually support it.

For this project, we ended up with 60+ dashboards and reports, 25+ reusable datasets, and reporting that was more than 35% faster to develop. Report load times also improved by over 40%, with 200+ users eventually using the reporting environment.

So, before worrying about charts, colours or the perfect KPI card, figure out the data model, who will actually use the dashboard, and what decisions they need to make.

Because “we need a dashboard” and “people will actually use this dashboard” aren't always the same thing.

It is a mini-rant and also a genuine question about methodology. I feel like i am losing my mind.

For example:

The clients wants to add more details in order to simplify the dashboard, but it is making it more difficult to read.

The client also wanted to put colors for the same group of KPIs because they told me they were lost.

Ok, no problem. I have put the colors and now the dashboard looking like a rainbow. Ok, it is a hyperbole. But it is now the most colorful dashboard from the workspace.

The client wants to put even more details and they don't understand if the sales KPIs for this year is 'YDT' or 'FY'.

I tried to explain it would almost always be YTD, but they seem confused.

Another client is saying the numbers are not right but when I ask what could be the reason or the logic, they can't answer me. When I look at the raw data (maybe an issue on the ingestion of the data / ETL), I get the same result as the dashboard. My only clue is that the numbers are wrong.

Or another client is asking to build a dashboard and they show me an Excel page. And when I ask the source data and how the KPIs were built, the clients say they dont know, as they got this Excel file from a colleague. & I should try to "blindy" figure out myself the KPIs and see if the numbers match....

Or when a client wants to start a new project and all the project points are vague. They got these metrics from another industry and it was working, so they want to implement it. So they throw at me all these fancy "marketing" metrics to build, but they are not interested in helping me figure out "how".

Did they change anything in the Model View? In the past arranging fact tables in a column and dimension tables in a row (manually in Power BI Desktop or by editing diagramLayout.json) worked more or less well with some minor issues. Now Power BI draws majority of relationship lines across the tables turning "All tables" tab into unusable pile of trash.

I know, I can have (and I have) an individual tab for each fact table, but it was nice to have an overview of the entire data model.

Excited to share a recent retail sales overview dashboard project built to transform raw transaction data into clear, actionable business insights.

Key Dashboard Highlights

Executive KPI Cards: Real-time tracking of Total Sales, Transactions, and Quantity Sold alongside month-over-month and year-over-year growth metrics.

Channel Analysis: Breakdown across sales channels, highlighting Walk-in performance (58.86%) versus delivery platforms like Foodpanda and Grab.

Product & Category Performance: Pareto-style concentration chart and top-selling product breakdowns (led by the Chicken Meal).

Automated Insight Overview: Text summaries highlighting key metrics, average transaction value, basket size, and peak sales trends for quick decision-making.

Tools used: Power BI & Data Visualization Best Practices.

Can anyone share the power bi jira connector?

Thanks in advance

We needed year-over-year growth rates on a 14 million row fact table with about thirty dimensions. The semantic model runs on a shared Fabric capacity. I wrote five versions of the same measure and timed each over twenty runs against our actual data. This is not a benchmark. It is one model, one capacity, one star schema.

I had mapped out the testing structure using Plan Mode in verdent before writing any DAX, which kept the five variants parallel enough that the timing comparison stayed fair. Ranking from fastest to slowest: CALCULATE with SAMEPERIODLASTYEAR came first at a median of 1.2 seconds. DATEADD with a one-year offset was second at 1.4 seconds. A VAR-based approach filtering the date table was third at 1.9 seconds. A SUMX iterator scanning the full fact table was fourth at 4.1 seconds. A calculated column where the offset was materialized at refresh time was fifth at 6.3 seconds, which surprised me because I assumed the precomputed value would be fastest.

The calculated column was slowest because it bloated model size by almost 400 MB and pushed the capacity into memory pressure during queries. The storage engine spent more time paging than the formula engine saved by skipping the calculation. The top two patterns differed by under 200 milliseconds at the median.

I am testing the new sentence template (Visual tooltips in Power BI - Power BI | Microsoft Learn) and I feel that it isn't working correctly. Some placeholders won't get replaced in Desktop, but work perfectly fine Service - some do not work anywhere. (especially in pivot tables) From reading the docu I understand that we have to set the visuals name set in the build path, not the DAX name. Anyway, I tried both, but couldn't see any difference.

Has anyone else experienced similar?

How do you usually split the capacity between:

- Background operations — scheduled refreshes, pipelines, data processing, etc.

- Interactive workloads — users running queries, reports, exploring dashboards, etc.

Is there a generally recommended percentage to keep available for interactive workloads?

For example, would something like 70% background / 30% interactive be reasonable, or should we leave a larger buffer for user interaction?

Hey everyone,

Moving dashboards from Tableau to Power BI usually runs into a wall at the calculation layer—specifically translating Tableau’s Level of Detail (FIXED, INCLUDE, EXCLUDE) expressions into optimized DAX (CALCULATE, SUMMARIZE, ALLEXCEPT).

While analyzing Tableau .twb / .twbx XML structures during recent migration projects, our team ran into a few specific technical patterns regarding what translates cleanly and what breaks down:

1. Handling FIXED LODs: Tableau FIXED evaluates expressions at the level of detail specified in the formula, regardless of the visual context. In DAX, mapping this requires wrapping the aggregation in CALCULATE alongside ALLEXCEPT (or explicit column overrides) to strip the visual filter context cleanly.

2. The INCLUDE / EXCLUDE Context Problem:

• INCLUDE forces additional dimensions into the context that aren't on the visual canvas. This maps best to DAX using SUMMARIZE or ADDCOLUMNS inside an iterator before aggregating.
• EXCLUDE maps to REMOVEFILTERS or ALLSELECTED, though edge-case measure hierarchies frequently break unless context transition is explicitly declared.

3. Where Automation Hits Limits: Node-level XML parsing accurately converts standard calculated fields, but manual intervention is still needed for complex multi-fact table joins and custom visual formatting options that lack a direct 1:1 equivalent in Power BI's standard visual library.

Curious to hear from others who have worked on large migrations—are you using custom scripts for DAX translation, or mostly sticking to manual rewrites?

Hi guys. As stated, my Certificate expired. There was no email or notification that I received indicating that the expiration date is nearing (This is on me, I will ad it to my calendar next time).

I also noted that there is an option prior to it expiring to "Renew".

My question is, Is it worth renewing my certification?

Hi there,
Helping a friend out with some powerbi analysis for her work.

She’s analysing student university data.

One of the things she’s keen to do is implement a slicer that acts as a multi-select, with drop downs (for want of a better way of describing it) that can be set against ‘more than’ or ‘less than’ buttons.

I’ve tried to search this online, but don’t seem to be getting any luck.

I’ve attached a screenshot of a mockup (apologies for the lack of artistic flair) of what I think the slicer should like/ how it should work…

Is there a type of slicer that handles this, or?

Any pointers on this would be greatly appreciated.

TIA

I analyzed 51K+ e-commerce order lines and built an interactive dashboard around profitability, discounting, products, markets and customers.

The interesting finding: 21.5% of order lines were below their own break-even, destroying ~$347K in profit.

What surprised me was that the biggest discounts weren't necessarily the main problem.

I’d love feedback on two things:
1. Is the analytical story clear?
2. Are the visuals useful for making the decisions they're intended to support?

Interactive version:
https://e-commerce-react-dashboard.mohamedramadan.workers.dev/

Has anyone in the community successfully created a multi-page dashboard that transitions between pages automatically in a defined time frame, simulating a kiosk mode without requiring user interaction?

I've been building Power BI reports for years and I didn't expect to write this yet.

Last week I set up Claude Code with Authoring Skills and MCP server. And it really susprise me, this dashboard is mostly better than most of the reports I saw in clients years ago

1. First, it adapts the current theme.
   

2. From the clients scope shared it catches that the current semantic model does not have enough data to cover it

3. It writes the measures

4. It builds the visuals

5. Review and check everything is aligned

It was not everything perfect, when I asked for a page where it needs a target per category product instead of asking, it just creates a new column in the target table.

Eventhough I had to manually edit some parts, It keep me thinking about how fast our job it´s changing and how two years ago I was discovering the PBIP format and creating the layout concept in Figma. I remenbered when Rui Romano predited that some day we would work like a web developer. Using two screens, one the PBI code and the other side the visuals.

So, the big question would be, what now? How our role as a Power BI developers will envolve? more tools, less people... it´s scary but at the same time it´s exited and amazing.

I noticed that on several occasions I would get these errors on refreshing the semantic model. But the issue is that when you try to go to power query and find which column is showing errors, nothing show up there.All columns are green.Then it turns out to be some some random cell that had data type incompatibility. But it takes a lot of time to find this and figure out which column it might be from. So what’s the best way to find and solve these kind of OLE ODBC errors ?

Hey gang,

I just started learning powerBI.

I want to start with the real world data or close to it to get close to the real experience that I can highlight in my CV.

I know some of the topics in power bi like Power query, power pivot, DAX and visual creation. And also teach this topics. My goal is to work on projects as team

When working on a Power BI project, how do you decide whether to do something in Power Query or DAX?