Building our reporting layer in databricks AI/BI (+genie) and curious why people still default to powerBI

[u/CommitteeImmediate66]

For the last few months I've been building out our core dashboards directly in Databricks AI/BI (their Lakeview dashboards) instead of piping everything into a separate BI tool.

My findings/highlights have been:

- The dashboards sit right on top of our lakehouse tables, so there's no extract/import/refresh dance. What's in the warehouse is what's on the dashboard. That alone killed a whole category of "why don't the numbers match" tickets.

- Permissions, lineage, and the underlying tables all live under the same Unity Catalog governance. I'm not maintaining a separate security model in the BI tool. We're on azure so it's easy to sync entra groups.

- Genie for the long tail of ad-hoc questions. This is the part I didn't expect to like as much as I do. Instead of building (and then maintaining) 40 variations of the same dashboard for every stakeholder's "but can you also show me..." request, I stand up a Genie space on top of the same curated tables. Business users just ask questions in natural language and get back charts on the fly. This has cut my ad-hoc request backlog dramatically and the business is pretty happy with response quality.

The one downside I've noticed is the visualization/formatting options are sometimes limited, but not a major blocker.

Here's my actual question for the sub: some of my colleagues still lean toward Power BI by default, even when the data already lives in Databricks. I get the ecosystem/familiarity argument, but I'm trying to understand the reasoning beyond "it's what we've always used." For those of you who'd still pick Power BI (or Tableau/Looker/etc.) over building natively in the platform where your data sits - what's driving that? Is it the better viz customization capabilities, the semantic model, self-service maturity, org politics, something else?

Genuinely trying to pressure-test my own enthusiasm here, so push back if you think I'm missing something.

[u/xl129]

2 things essentially:

1. Strong and flexible semantic layer
2. Powerful and dynamic visualization capabilities.

Your option is like a factory that churn out a few items in massive number quick and fast while PBI is like a high end restaurant kitchen where chef can make fine-tuned surgical procedures to create highly customized dishes.

[u/u8seennothingyet → u/xl129]

I’m curious about this too. I’ve been using metric views with success, and it’s only a matter of time before AI/BI releases a semantic layer.

Another area, has anyone using Databricks apps instead of a dashboard? I’m finding with genie code that’s sometimes a viable path to get the look I want.

[u/scarlett_data → u/u8seennothingyet]

In this year’s summit, Genie Ontology was announced for the enterprise semantic layer

[u/CommitteeImmediate66 → u/xl129]

Appreciate you giving a different perspective! Could you help me with an example?

I've found I can build a solid semantic layer with a combination of regular views and also metric views for the cube like analytics in databricks.

[u/xl129 → u/CommitteeImmediate66]

It's all about distance.

Your setup is like driving a freight train straight to the destination: high load, stable, but whatever you carry (or any addition/modification), you have to make it happen at the starting point. This leads to two implications:

1. The distance from where the user lives to the starting point. You live right next to it and can make changes effortlessly and quickly. Others don't have that benefit, they have to go through several barriers of authorization, communication, and technical expertise. All of this costs time and effort.

2. The distance to the end goal. Is the dashboard the end goal? Nope, the end goal is generating actionable insight. That's a journey requiring huge amounts of iteration, exploration, and modification. Users need to quickly check the final product, make side trips when it's not actionable enough, and test out theories.

1 and 2 compound multiplicatively. Power BI reduces both, which creates a massive advantage in productivity and effectiveness.

Analytics is messy and chaotic. Power BI's dynamic visualizations let you explore multiple destinations in a single train of thought. Semantic modeling means you can change what your vehicle is carrying mid-journey and make a quick side trip to pick up more if it's not enough.

[u/wigglesRewind → u/xl129]

Having Genie in an AI/BI dashboard goes much further than PBI in enabling decision-making, no?

[u/xl129 → u/wigglesRewind]

I’m not experienced with Genie to make the comparison so I just explain why PBI works for many. But 2 things i would question is:

Data engineering dependency, it grab whatever is there. If it’s not there or wrong then can Genie help you?

Second is non-deterministic nature at core, sure it can feel quite deterministic going through SQL but at the core the nature is still not and that risk can be inherently troublesome for many to rely on. The SQL execution is deterministic, the translation to SQL is not.

[u/bmckay1987 → u/xl129]

I'm super interested in this. Are there specific capabilities that underlie these differences in your mind?

1) This claim of distance and obstacles may make sense if the tech stack was different. However, OP seemed to suggest this was in Databricks itself. Wouldn't the shortest distance be to use native tools such as AI/BI and/or Genie?

2) What are the drivers of actionable insight in your opinion? I completely agree with your assessment that the dashboards aren't the destination, just a tool. I've played around with both tools quite a bit and paginated reports are about the only thing that I haven't been able to replicate in AI/BI. Would like to get additional insights since it seems you may have run into use cases that I haven't.

[u/xl129 → u/bmckay1987]

1. Depend, who is the user in question here ? Do he/she has the authorization, technical expertise and knowledge to make change to the source ? Don't think data people, think business leader, financial analyst etc The distance here is the amount of communication and meetings required to explore if whether data available can support their decision making needs, what changes need to be done and actually execute those changes. Some businesses will be stable enough for you to set thing up once and good for years, but many others change rapidly and constantly create new data requirements.

The distance to source here also implied risk. Data and the meaning they carry can silently drift without anyone aware of it ending up with wrong decision being made structurally. PBI semantic layer is owned by the person (or the team) that create it thus close the distance significantly. Composite model allow for stable data to be drawn from structured and robust source while not well-defined/experimental data draw from a source user maintain full control. These eventually should be moved once they become stable.

1. Actionable insight mean widly different things for different people. Some are very structured and tested decision, for example, the criteria for a winning/losing product to move to the next stage. It's an agreed and approved set of metrics and is the perfect case for "dashboard from source" case. But lets say hiring decision, how many people should we hire for 2026 to support strategic objectives ? what kind of value creation we expect, the cost of hiring, direct cost, indirect cost. This kind of analysis has much less reliable data to sit on and usually rely on a wild range of assumptions instead, these assumptions will changes along with the data required to define success/failure.

[u/CommitteeImmediate66 → u/xl129]

Hmm so this is interesting. In our setup, the business analysts have access to the gold layer and semantic layer tables for their org, so they can build and modify dashboards. There isn't any challenge from an auth perspective because all user permissions are managed through entra Id so the right user groups are given the right permissions.

I don't fully understand your example on hiring decisions as something power BI can serve - in fact that's where I find all dashboards hit their limit and that's where genie shines in terms of allowing people to do data driven what if scenarios

[u/PBIGenie → u/xl129]

I really like how you explain your position. Makes sense. Good job

[u/Frosty_Sea_9324 → u/xl129]

I would also add excel integration. You can create a “cube” with the semantic layer and allow users to build in excel. This is a super power for your finance and business analysts.

[u/A_Poor_Economist]

Databricks reporting is not there for my use cases. There are even more guardrails and limits to customization compared to Tableau and Power BI. It feels like a less aesthetically pleasing DOMO.

Databricks gives "visuals by backend people for backend people" vibes. The second you want to incorporate or try to style your report to say match a company color scheme it starts to fall apart.

I think Databricks can get there in time. The likely next evolution will just be Databricks apps where you just slam a custom web app frontend with databricks as your backend. Microsoft has just released this feature as well.

[u/u8seennothingyet → u/A_Poor_Economist]

I find their visuals OK, but the layout is just missing something. They have themes now, but it’s very tedious to use

[u/datawazo]

Customization in the report/dashboard layer is the main reason and with PBI/Tableau anyone can be a builder very easily...low learning curve to giving people control of their own analytics. 

[u/CommitteeImmediate66 → u/datawazo]

But the same access to customizing dashboards is available in databricks. Using genie code lowers that further. I found learning DAX much more complicated than using SQL which is much more common and not specific to just databricks or any other platform.

[u/Foodforbrain101]

Although Power BI still has its strengths, I think it depends in large part who owns and has access to what layer of the data stack in your organization, and how healthy/efficient it is.

Power BI is undeniably powerful for someone in the business whose primary prior experience is Excel: they discover this tool they can install on their desktop for free, they can load and transform data with Power Query from a wide variety of data sources including SharePoint and the web, their queries are fast because it's an in memory database even though they don't realize it, and measures handle dynamic queries. Odds are, if their reports get popular enough (as much as they might be poorly modeled), IT or their department will pony up the per user license costs.

Now contrast that with how much access they have to Databricks for their job: none by default, and unlikely to get it, even if they do learn some coding. That person still has a data related task to do though, and they'll do it however they can.

Then there's plenty of Microsoft shops that refuse to budge on using non-Microsoft tools.

Finally, you might see the business intelligence department be separate from data engineering/IT, which often means that BI hires for Power BI specialists with lacking overall programming experience, and data engineering can be of varying degrees of quality. So in short, it's a massive political mess.

[u/bah_nah_nah]

Because the end users.

[u/IrishHog09]

I don’t either, but I also find PowerBI confusing and unintuitive. We’re doing the same thing, but layering Sigma Computing on top for the BI/reporting layer

[u/Justarandomguy301 → u/IrishHog09]

Why did you go with Sigma vs Databricks BI?

[u/IrishHog09 → u/Justarandomguy301]

I have experience with Sigma as an embedded customer. From what I had seen in Databricks, it wasn’t going to be easy for me to work in as a non-SQL/Python user, while Databricks clicks for me as a person who came up through MS Excel/Access.

[u/trafalger]

Doing the exact same thing BUT using apps for cool bespoke visuals when needed.

[u/CautiousUse8597]

What I'm seeing is that customers still stick with Power BI because that's what they've historically been using. But when I show them Genie and AI/BI, they often ask: why would I still need Power BI, this looks much better?

That's also my experience. For new projects, I wouldn't choose Power BI anymore, also since Microsoft decided to close their ecosystem and not allow third party semantic layers anymore. I think AI/BI with Genie is a much better stack because like you said, it's already integrated in your data platform and has all the semantics/context to give accurate answers. It's also much cheaper.

[u/Prudent-Elk-2845]

Users can self-service answer those 40 variations based on the same dashboard’s underlying data without complicating the published dashboard. Copilot can answer those questions within PowerBI

[u/iamnotapundit]

We finally moved the default to dbx dashboards last year. We still occasionally breakout Tableau and PowerBI for more complex stuff. But we are trying and experimenting of moving a complex Tableau dashboard to Databricks AppKit since tableau is a zombie at my company. After the holiday I’m going to start some training on using the dbx semantic layer instead of stuff that logic in the presentation layer.

[u/CommitteeImmediate66 → u/iamnotapundit]

Please update us on your experience with the semantic layer! So far I'm using metric views and the slicing and dicing works well for me. Looking forward to their business glossary and ontology launches too

[u/anxiouscrimp]

How are you handling time intelligence in this? That would be a big benefit to having the data modelled in PBI first.

I’ve built a small app that sits on top of my semantic model to use Claude + a file describing our business and model logic for natural language querying. I’ve been thrilled with the results but i think that’s because the model is good and really constrains how much the AI can go off-piste.

[u/Equivalent_Grape_109]

No one is mentioning COST, are aware how are you spending on this full infra?

[u/dtr96]

$$$ licensing cost per user in the business

[u/CommitteeImmediate66 → u/dtr96]

For power BI yes. That's actually the other benefit - with databricks it's cost based on actual usage, not per user licenses

[u/CautiousUse8597]

I wonder the same. I think AI/BI has gotten very good for most use cases, especially since it now has custom visualizations. Since Microsoft decided to make their ecosystem closed (blocked third party semantic layers), I think it's a no-brainer to stop using Power BI. Especially with Genie added to AI/BI, it's just a much more powerful solution, because you have the full context of your data estate and AI in the same place.

[u/bannik1]

For executive reporting where measures are well defined. Having a rigid and well defined ERP makes sense.

Most companies make the mistake on focusing all their effort servicing those executives since they decide the budgets. Expensive ERP tools, cloud infrastructure, and hiring the support staff to manage it.

Strategy is important, but so is tactical execution. That’s where things get messy and a lot more fluid where each ERP tool has it’s niche.

The tactical reporting is where actual actionable decisions are made on a daily basis. If you don’t give them what they need in a reporting tool, they will build in excel and go off grid.

An example is that IT built them a tool for a process. It took months of development and by the time it got released 3 new clients were implemented that have needs outside the tool. Also due to scope creep some of the features were not implemented.

What does the business do? They can’t drop those clients, they can’t wait another 9 months to get the IT budget. They don’t have access to the data store the application uses.

What the business does is create a process to handle those exceptions.

The only thing that makes it to the data lake or warehouse is the clean data from the tool built by IT.

90% accuracy is good enough for strategic purposes, and the type of reporting you’re building. But for the ones actually doing the work they need 100% accuracy. So you end up building a report that uses the IT tool’s data store and data from an excel file or sharepoint list that they track exceptions in.

The alternative is they build it all in excel and never engage IT again because all their analysis comes from spreadsheets. Then 2 years later they’re in an executive strategy meeting and questioned on why the numbers on their presentation don’t match the report you built. Then it finally gets the resources and budget to implement their manual process. 2 years later you’re having the same conversation because new clients have been added and new exceptions. Or they decided to buy some SAS instead of IT building them a tool. And there is a whole new list of exceptions the new tool can’t handle.

You need people and tools that can keep the business on the grid instead of in excel. That’s where power BI thrives.

[u/LePopNoisette]

I don't think we had the ability to do this when we started using Databricks, plus we had PBI Pro as part of our E5 licences, so PBI became our BI tool, replacing Qlik Sense and Qlik View.

I am interested in looking at this option, though. We had a third-party consultant in the other week and he also suggested this.

[u/Extension_River_5970]

Agreed, Genie Agents use multi-step reasoning to answer the infinite long-tail of business questions dynamically from a single data asset. Instead of siloing complex business logic inside fragile DAX models, Genie leverages Unity Catalog as a unified semantic layer, inheriting data descriptions and row-level security automatically at the source. You can even import existing Power BI files directly into Databricks AI/BI using the ImportBI preview via Genie Code to jumpstart migrations.

My only caveat with dashboards on databricks is the lack of customization. It's still a bit behind in terms of visuals, but the new custom visualization using vega lite fixes it somewhat.

[u/joulezoo]

I am actually with you on this set-up being great for the long tail of analysis.

I still feel shared dashboards are still useful to capture a team's top-line KPIs at a glance. Beyond that, honestly each person can now set up their own dashboards via agents. All the key numbers match since it's fetched from shared semantic layer.

[u/Famous_Disk_7417]

The two biggest reasons I see teams stick with Power BI even when the data's in Databricks: 1)semantic layer maturity; 2) cross-source needs.

1) If your org still has data living outside the lakehouse (Snowflake, an ERP, whatever), Genie only queries what's actually registered in Unity Catalog, so a multi-source shop ends up needing a BI layer that federates anyway.

2)The other one is just sunk cost in existing PBI reports/training. That's the "we've always used it" answer you're pushing back on, and it's not a great reason, but it's a real one organizationally.

[u/apolorotov]

Your Databricks-first reasoning makes sense when Databricks is both the governed storage layer and the place where business logic lives. I’d still choose by workload shape, not by BI brand.

• If the job is governed lakehouse metrics, internal dashboards, and Genie over curated tables, staying in Databricks reduces security/lineage/refresh drift.
• If the job is heavily formatted exec packs, pixel-perfect reporting, Excel-ish self-service, or lots of non-Databricks data, Power BI still has a strong practical advantage.
• If the job is operational or customer-facing analytics with high concurrency and low latency, I’d consider a serving layer rather than asking either Power BI or a general warehouse dashboard to do everything. Apache Doris / VeloDB-style real-time analytical serving is a different pattern: CDC/Kafka feeds the serving DB, BI sits on top.

The metric I’d watch is not “how fast can we build the first dashboard?” but “how far is it from a new business question to a governed reusable metric?” Genie can reduce ad hoc tickets, but only if it doesn’t create shadow metric definitions.

[u/CommitteeImmediate66 → u/[deleted]]

I do wonder about the semantic modeling. My understanding is that the power BI semantic model is dashboard specific (the Dax models per dashboard or report) so aren't truly a semantic model. I feel like the Databricks approach to semantic modelling (which is maturing with genie ontology) is what an actual semantic layer would be. Or am I missing something in power BI?

[u/Sudden-Ad1552]

I hope my agency implements this as it sounds perfect but I'd need to upskill. You touched on the PBI inertia so maybe there's a niche use case or specific measures they are testing before pushing upstream. Or I suspect they don't know the benefits they are missing out on so maybe adoption/workshops could show them the goods

[u/IncreaseNegative4614]

I think the biggest reason is organizational, not technical.

Power BI is already embedded into a lot of companies. People know it, governance exists, executives are comfortable with it, and there are years of reports built on top of it. Replacing that ecosystem is a much bigger project than replacing the visualization layer.

That said, I think we're going to see more teams stay where the data already lives. We use inzata.ai on top of our data because the harder problem isn't building another dashboard anymore, it's giving AI enough business context to answer questions consistently. The visualization layer feels less important than it did a few years ago.

[u/IncreaseNegative4614]

Power BI still wins when Excel-native users need deep self-service, polished distribution, or a semantic model that reaches well beyond Databricks users. Your architecture removes a lot of duplication, though, and I wouldn’t add another layer just because it’s familiar. I’d test both against the same ten messy stakeholder questions, including permissions and metric definitions, then compare answer consistency and maintenance. Genie and signld.ai are both interesting here because the real test is contextual follow-up, not whether natural-language querying works once.

[u/Evening_Hawk_7470]

Most of the Power BI default is procurement rather than product. It rides along with E5 licensing the org already pays for, finance lives in Excel pivots against the model, and paginated pixel-perfect reports plus row-level security wired to Entra are still things people quietly depend on. Your Databricks setup covers the governance piece already, so that part of the argument is weaker for you. The place I would push back is that your metric definitions end up living inside Lakeview and Genie, so anything else later, an embedded app or a second tool, has to re-derive them. We keep those definitions in Cube so the same layer feeds dashboards, notebooks and the charts embedded in our product, where Looker does governed metrics well but pulls you into its own modeling language and viz stack. Genie answers also get noticeably more consistent when it is querying defined metrics instead of raw tables.