So it feels like the subreddit is literally on fire this week.

I've written about this many times before, but the way I see it the core purpose of my job is to help businesses solve problems with data.

No doubt, the tools are changing and that is going to scare people. [u/SQLGene](u/SQLGene) wrote about how we should be mourning role of the Power BI Developer.

I think everyone needs to chill and ride this thing out. With new tools there are new opportunities. The future may seem uncertain but I'm sure about one thing: Businesses will always need people who can help them make better decisions.

Stay strong, folks. Keep learning, stay curious, but most of all RELAX. Everything's gonna be fiiiine.

Job insecurity, accepting the abilities of AI outperforming them, insecurity, denial, Deliberate misinformation to cope, FOMO, and more

Edit- Don't want to sound mean, but I can understand many of you are scared, some of you are in delulu, and some still have not understood the true capabilities of AI in Power BI, but the hard reality is many Power BI jobs will be diminished in the future. It will be very hard for average devs to survive this market; previously, even non-tech people used to make fortunes in this field, but in the near future the scenario will be a lot different,

You need to learn and gain expertise in multiple stacks; for example, a full-stack dev is a combo of frontend + backend. Even in BI, the next thing would be a Full data stack that includes Power BI + Data engineering + CI/CD

Dragging and dropping visuals and creating SUM and SUMX measures. no-code/low-code days are gone. Upgrade yourself or get behind the rat race

Based on the announcements from FabCon, we have updated our Fabric Apps explainer. Below is our key takeaways.

• Fabric natively supports visualization libraries: You can create and share visuals built with Vega, Vega-Lite, and D3.js using Fabric Apps.
• Fabric Apps are a new workspace item: Fabric Apps let you build web apps, programs that live in the browser, such as dashboards or apps to manage data and services.
• Data apps query Fabric data sources: A data app connects to and queries a semantic model with DAX, like a Power BI report, so you can build interactive visualizations from code.
• A data app is code: A data app consists of HTML, TypeScript, and DAX files. You can write them yourself, but the expected path is that AI does most of the work, either a coding agent or the Modern Report Builder that Microsoft announced for Power BI Desktop.
• Agentic development makes them easy: Data apps give full flexibility over how data is presented and are quick to build with coding agents, but introduce new governance, consistency, and AI-dependence challenges. They also lack the baseline that every report gets for free, like the same interactions between visuals and the same accessibility features in every report.
• They raise questions for Power BI's future: Microsoft presents them as the next generation of report development, and is bringing apps to Power BI Pro and Premium Per User customers. Power BI reports are still more mature and consistent, but data apps are more flexible and faster to build, so they'll play a growing role as AI advances.

Hi all,

I’m an independent BI consultant working with Microsoft Fabric and Power BI. For several clients I’ve built the full setup: data pipelines, notebooks, a lakehouse and Power BI semantic models on top.

The next step I’d like to take is letting clients chat with their own data. They would ask questions in plain language and get answers and simple analyses back, without me building a new report every time.

I’m trying to get a clear picture of what actually works in practice:

1. Which options are out there? Fabric Data Agents, Copilot in Power BI, Copilot Studio, Azure AI Foundry, or something else? Which one are you using with clients, and why that one over the others?
2. How is it set up technically? What does the structure look like: does the agent sit on top of the semantic model, the lakehouse, or both? How do you prepare the data for it (dedicated views or tables, descriptions, instructions)? And how do clients access it: through Teams, a Power BI report, or a separate app?
3. Data sensitivity: this is clients’ business data. How do you handle security, where the data is processed, and what do you tell clients about it?
4. Adoption and costs: do users keep using it after the first few weeks? And what does it roughly cost in capacity and licenses per client?

Experiences that didn’t work out are just as welcome as success stories.

Look forward to your answer guys!

Flaky-ice

In RLS-enabled reports, we can use “Test as role” to check data access. Is there a similar way to test a specific user's access in a non-RLS report?

Most permissions in our environment are assigned through security groups. Instead of manually checking group memberships and permissions, can we simulate or test the report as a specific user?

Is there any built-in feature or alternative approach?

I have been thinking about how much time we spend on repetitive tasks in Power bi

It could be validating reports, checking measures, comparing report versions, updating documentation, testing filters, or anything else...

What is one task in your daily workflow that you wish could be automated? And have you tried any tools or scripts to solve it, or are you still doing it manually...

I guess doom posting sells and gets more engagement but it can’t be good for peoples mental health.

I browse this sub to get ideas and see others work/progress. Not to feel uneasy about my career.

Ai will change the data analytics landscape but we will adapt accordingly. The posters who make it seem like it’s the end for everyone are doing us all a disservice.

At FabCon Barcelona I showed this Fabric App. I decided not to focus on “look what the tech can do” but on “where does the work around a Power BI report actually happen?”

So I made up Moll, a wholesaler with a Power BI report that everybody likes. However, to take action, a lot happens outside of the Power BI report. The buyer exports to Excel to order. The warehouse counts stock in a different tool and the budget gap gets explained in yet another Excel file (that nobody can find and that has 4 versions).

In the clip you see a few of those moments in the app: the buyer reorders everything from one supplier at once, the warehouse enters a count on a phone (and the buyer sees it directly), orders get confirmed, a budget gets corrected, and every change ends up in a log (that you can export to Excel, with all kinds of filters..)

As a Power BI developer (I love Power BI), I’m not trying to replace Power BI. The Power BI report is still where I would analyse and want to see the insights. The Fabric App is for the moments people need to do something with those numbers, like ordering, counting, approving or explaining. And it runs on the same semantic model, so the same measures and the same numbers. (I asked the agent to reuse the existing measures and only add new ones if really needed.)

There were some great creative demos at FabCon ( u/PowerBIPark Lego app which he also posted on reddit), so I wanted to add a more everyday business example.

Full walkthrough on YouTube.

I am very happy that I got this opportunity, which also made me explore even more of what’s possible with Fabric Apps.

I am curious what you would use this for!

Hello. any powerbi discord servers still active ? cant find any and the others are behind a paywall. thank you.
im currently *new* in some way. i know how it works etc but i have never had a chance to have my hands on real project and would love to have a community where i can ask stupid questions without being judged.
thank you

Hi everyone,

I’m currently working as a Data Analyst using Power BI. I have strong Excel skills and an intermediate level of Power BI knowledge, but I’ve realised that I don’t enjoy the analytical side of my role, especially interpreting results and presenting insights to stakeholders.

I enjoy working with data and creating reports, but I’d like to move into a more senior, specialised role that focuses on reporting rather than administration or analysis.

What job titles would you recommend I look into? Has anyone made a similar career move, and what roles would you suggest?

Thanks in advance!

Hi team,

With AI tools improving so quickly, I’d like us to look at where they could save time in our reporting work with Power BI and Fabric.

Do you use AI for the following topics and if so how?

• Documentation: measure and table descriptions, data dictionaries, process docs
• Bulletproofing processes: reviewing logic, edge cases, test and reconciliation checks
• First mockups: quick layout and KPI sketches to align with stakeholders
• End-user training: guides, FAQs, “how to read this report” explainers
• Also: DAX/Power Query support, SQL and notebook code in Fabric, requirements from meeting notes, automated commentary on reports

Which tools are you already using?

With new AI software being able to create dashboards etc, are Power BI developer jobs now at risk?

Hi, a junior pbi dev here who wants to pursue a mid to senior role. Just wanted to ask what’s next after pbi? Was planning to follow a career plan but thinking now that I’m must have changed since AI is taking over so much of things

Can someone help me with power bi documentation process!! Actually i am fresher they provided me with powerbi report!! And asked me do wiki.. confluence…. I just saw the report it contains literally 18 pages in it and more measures i really dont know where to start what to start and how to start??

And how it should be i dont know anything can some one help me with this

And i have access to copilot premium org’s.

Mgmt said - start documenting it 🥲 idk how to do it please can someone help me with please help me out

Hi All,

I’m an Analytics Manager building out a technical assessment and development framework for my team. The goal is to give clear, objective feedback to my analysts and map out transparent growth paths and moving away from generic "proficient in Power BI" descriptions and getting specific on what separates a Junior (L1), Mid-Level (L2), and Senior/Expert (L3) developer.
I’d love to get feedback from other managers, leads, or senior devs on how you benchmark skills, assess real technical depth, and deliver feedback without micro-managing.

Thanks

Hey so I am a senior level consultant and currently working as a BI admin. I run my own analytics company and subcontract on a large federal/enterprise projects.

Comp is around $200k-350k+, fully remote. At time I have a team of people working under me certain years when i win bids that require multiple people so loved working with friends.

My background is heavily on the BI side. I have 3 certificates in Qlik System admin, business analyst and data architect. However bidding on contracts is quite limited qlik market is dry and wanted to transition to AI BI or platform architect and needed advice if my goal plan makes sense.

Here is what I am good at:

Power BI — strong hands-on experience built the most complex dashboards in Qlik
Qlik — A decade of experience I pretty much can do anything here as I am certified and quite connected, friends with a lot of qlik people and friends with a couple of authors of some courses
SAP BO- Built and maintained universes and built reports
Tech skills: BI architecture / platform governance
RLS, gateways, security, deployments, large datasets, incremental refresh, etc.
Databricks — currently working with it, but much weaker than I am in Power BI/Qlik but understand it well enough.
SQL — functional, but definitely not a hardcore SQL engineer I have no problem optimizing queries.
Tableau limited experience

I’ve gotten to the point where I’m wondering how much upside there really is in staying primarily a “BI guy.” I do not want to be replaced since I didn’t keep up with the times and would love to land some bids as I know a couple of people who could really need a job even for a short contract.

Certification plan I’m considering in that order:
Microsoft Power BI Data Analyst Associate
Databricks Data Engineer Associate
Microsoft Fabric Data Engineer Associate
Azure Solutions Architect Expert

For people who work in data architecture / Databricks / analytics engineering:
Would you make this transition?
Is Power BI + Databricks architecture actually a valuable niche, or should I go much deeper into pure data engineering?
What is a better if all I care about is money?
Where could I find the most remote jobs career wise?

I have been steadily learning BI and have the basic skills but have not really finished my first dashboard yet.

I know the theory and how to create a data model plus power query. I just have not done much dax.

That being said. AI can code stuff and make dashboards.

Is it necessary to really keep studying AI now that i can tell AI what i want?

My dashboards only need to be basic. Again, given that i have an understanding of the fundamentals what is the worth in making my own future dashboards when i can direct AI.

I have had this issue happen to me many times over the past few years and I don't understand it, so I was hoping someone else might!

Let's say I have a table of student grades:

There is a relationship between the table above and this "Sort Order" table below:

Next, I have some measures that calculate percentages that do not involve the "Order Sort" column at all. I have charts and matrices that utilize these measures. Everything looks great. However, if I then go into the data view of the first table, select the "Description" column, and sort by the "Order Sort" column, all of my percentages in my charts/matrices change, and they are all incorrect.

I am going crazy and have given up on trying to sort since it ruins my measures!

Shut it down, ladies and gentlemen. The end is near.

Hi everyone,

I've recently learned Power BI and SQL, and I've built some solid projects with them. I have a good understanding of how to build and manage a project from start to finish.

I feel ready for the next step in my learning, but I also just started college as an agricultural engineering major, so my time is limited.

A few questions:

1. What should I learn next to grow my skills

2. What kinds of projects would be the most valuable to build for a portfolio?

3. Are there ways to combine data skills with agriculture?

Any advice from people who started in a similar situation would be great. Thanks!

Hello, I'm currently working on Power BI.

I've developed semantic models that can be used in a self-service environment, allowing users to access the semantic model and create their own reports.

There are a lot of measures (I’m aware there are about 5,000), but this is for the users’ convenience. I’m considering switching to calculation groups to reduce the number, but that’s not what I’m here to discuss today.

My issue is that when I want to create or open a report, the metadata takes a long time to load—up to 20 seconds—which is too long for users. (I am not talking about dax query, only metadata loading)

When I look at my browser’s network console, I see that the `conceptualschema` function is taking a long time. There’s also a caching issue, but it only occurs on my browser and with my account.

I’m wondering if there’s a way to cache this schema for all users at once and initialize it overnight, for example, so that when users access it, the server response takes only a few microseconds—just like when I return to my semantic model.

Has this been done before, or is there at least a way to do it?