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Can Power Automate Automatically Retry a Power BI Dataset Refresh Until It Succeeds?

manoj_0911 — Fri, 18 Sep 2026 14:32:32 GMT

Hi Power BI Community,

We currently perform a manual refresh of a Power BI dataset in our Production workspace. We would like to automate this using Power Automate.

Our requirement is:

Dataset: Recording
Workspace: Production
Required to be successfully refreshed before 8:00 AM EST every day
Start the refresh automatically, for example around 6:00 AM EST
Sometimes the refresh fails intermittently, so we would like Power Automate to retry the refresh if it fails
Once the refresh successfully completes, the flow should stop immediately and should not trigger any further refreshes
We don't need a success email/notification.
If the refresh continues to fail after a predefined number of retries, we would like to be notified.

Our intended flow is roughly:

Scheduled trigger – 6:00 AM EST ↓ Refresh Power BI Dataset ↓ Check actual refresh status ↓ Completed? ↓ YES ↓ NO / FAILED STOP Wait ↓ Retry ↓ Check status again ↓ SUCCESS → STOP

Questions:

Can this be implemented reliably using the Power BI connector in Power Automate?
Is there an action/API that allows us to check the actual dataset refresh status after triggering the refresh?
How can we make the flow retry only when the refresh fails or does not complete?
Once the refresh succeeds, how can we ensure the flow stops and does not trigger another refresh?
Is there any limitation on repeatedly triggering dataset refreshes through Power Automate that we should be aware of?
Would you recommend using the Power BI REST API instead of the Power BI connector for this scenario?
What Power BI and Power Automate licenses are required for this solution?

Any guidance or examples of a recommended Power Automate design would be appreciated.

Thanks!

Building an AI-powered quote engine on Microsoft Fabric

Avyaktha — Fri, 18 Sep 2026 14:15:20 GMT

The problem

Sales teams for technical products often get free-text enquiries like:

"I need 4 x 5T chain hoists, 10m lift, for offshore use"

Turning that into a quote usually means someone manually matching the product, checking the bill of materials, checking stock, checking supplier lead times, and pricing it up. I wanted to see how far Fabric's native AI capability could get toward automating that whole chain, without reaching for an external LLM service.

What I built

A proof of concept entirely inside one Fabric workspace:

A Lakehouse holding a synthetic but realistic-looking dataset: a product catalogue (chain hoists, wire rope hoists, lever hoists, beam clamps, trolleys), a bill-of-materials per product, inventory levels, supplier lead times, and pricing
A set of notebooks using Fabric's built-in AI Functions to read a customer enquiry and turn it into a structured, priced draft quote — matching the enquiry to a product, exploding the BOM by order quantity, checking stock, and rolling up a price and lead time
A simple interactive demo — type an enquiry into a text box, click a button, get a quote back — running entirely inside a notebook, no external app or Azure OpenAI resource needed

I deliberately made some components tight on stock (gearboxes, motors, brakes — the genuinely long-lead-time parts), so the demo shows a real "can we fulfil this now, or is it pending procurement?" decision, not just a happy path.

The interesting part

Real enquiries aren't always single, clean requests — customers often ask for a few different things in one message ("2 hoists, one standard and one offshore"). The AI extraction naturally returns one value per field, so it initially collapsed multi-item enquiries into a single line and silently dropped part of the request. Getting a reliable split-then-process approach working — without it silently swallowing half the enquiry — was the trickiest part to get right.

A couple of smaller but important lessons: null/missing values from the AI extraction need explicit handling in code rather than relying on prompt instructions alone, and discontinued or not-yet-launched products need to be filtered out at the matching stage so the system doesn't quietly quote something no longer sold.

Happy to go into more detail on the data model or the AI extraction approach if anyone's tackling something similar on Fabric.

Export dashboards created in Power BI

Anas2 — Fri, 18 Sep 2026 13:15:38 GMT

Hello,

is it possible to export dashboards created in Power BI to a PowerPoint file or an interactive PDF ? if it's the case, how to do it ?
Thank's

Data Lineage support for Notebooks

JulianThomas — Fri, 18 Sep 2026 12:55:44 GMT

I would like to be able to utilise and benefit from the Data Catalog Data Lineage, even when I am not utilising the required Pipeline Activities that generate Data Lineage metadata.

I think this should be easy to do, if we can expose at least parts of the the api via python, so that when we are creating notebooks, we can setup similar metadata to our pipelines, allowing notebooks to be included in the data lineage. Possibly even allow this for SQL type code that could be executed as well.

Display Table[Column] instead of Column Name in Conditional Formatting Dialogs

Manickambeeie — Fri, 18 Sep 2026 12:03:31 GMT

In large-scale Power BI semantic models, it is common to have identical column names across multiple fact and dimension tables. Examples include columns such as Status, Category, Region, Employee ID, Created Date, and Priority.

When configuring Conditional Formatting (Icons, Font Color, Background Color, Data Bars, Field Value, Rules, etc.), Power BI often displays only the column name and does not clearly indicate the source table.

Displaying Table[Column] would improve report governance, debugging, and developer productivity.

Native SAP SuccessFactors and Active Directory Connectivity for Simplified Data Integration

João_Cristo — Fri, 18 Sep 2026 11:25:04 GMT

Having native connectors for SAP SuccessFactors and Active Directory would eliminate the need for workarounds such as notebooks, custom scripts, or complex integration setups. Users should be able to create a standard connection and use Copy Data pipelines directly, just like with other supported data sources. This would simplify data ingestion, reduce implementation effort, improve reliability, and make HR and identity data integration much easier to manage in Microsoft Fabric.

Error while exporting Excel using the "Summarize Data" option.

AAM_ — Fri, 18 Sep 2026 11:12:59 GMT

Hi,

When trying to export to Excel, some users are receiving the following error message:

No changes have been made to the report or to the underlying data model. However, starting today, some users have begun encountering this error.

The issue does not occur consistently, and it is not associated with any specific combination of measures and dimensions.

We are unsure what could have caused such a sudden issue.

thanks!

Allow Azure Databricks streaming tables to be mirrored into Fabric

_taab — Fri, 18 Sep 2026 10:13:41 GMT

Mirroring an Azure Databricks Unity Catalog into Fabric silently drops streaming tables. They are not greyed out, not flagged, not explained. They simply do not appear in the picker, so there is no way to select them. We have tested this and the documentation confirms it: materialized views and streaming tables will not be displayed.

The reason this is hard to accept is that the data already qualifies.

Mirroring for Azure Databricks does not copy anything. It creates OneLake shortcuts to the Delta data in place, which is exactly why Microsoft markets it as unified access without data duplication. So the only question that should matter is whether the object is a Delta table in Unity Catalog.

It is. Databricks documentation states that all Unity Catalog managed tables and streaming tables are Delta tables, and that a streaming table is a Unity Catalog managed table with extra support for incremental processing. Microsoft's own Learn documentation for Azure Databricks describes streaming tables as Lakeflow declarative pipeline datasets backed by Delta Lake. Two sets of Microsoft-published docs say these are Delta tables, and the third filters them out of the mirroring picker with no stated technical reason.

This is not a niche object type. Every output of a Lakeflow or DLT pipeline is a streaming table, and declarative pipelines are the pattern Databricks recommends for building curated silver and gold layers. The practical result is that the tables an organisation most wants to serve to Power BI, semantic models and data science workloads are precisely the ones mirroring refuses to show. A huge share of our own curated estate is unreachable from Fabric for this single reason.

The only workaround is to write jobs that copy every streaming table into a plain Delta table so the picker will accept it. Duplicate storage, ongoing compute to keep the copies current, latency added on top of pipelines built for incremental processing, and governed data existing twice with the second copy outside the lineage of the pipeline that produced it. We are paying, in cash and in engineering time, to reintroduce exactly the duplication that mirroring was built to eliminate.

The ask:
Include streaming tables in the mirrored catalog picker and shortcut them like any other Unity Catalog Delta table. If there is a genuine constraint, for example around managed storage paths or pipeline maintenance operations rewriting files, then say so in the documentation and give us opt-in support with that caveat written down. Right now the limitations page lists streaming tables alongside Delta sharing tables and RLS-protected tables with no explanation of why, and teams only discover the gap after they have committed to the architecture.

Fabric is meant to be where the whole estate comes together. For anyone running declarative pipelines on Databricks, this one exclusion decides whether that is true or not.

References:
https://learn.microsoft.com/en-us/fabric/mirroring/azure-databricks-limitations
https://learn.microsoft.com/en-us/fabric/mirroring/azure-databricks
https://learn.microsoft.com/en-us/azure/databricks/tables/delta-table

Power BI, import report and semantic model: show change connection pop-up

Reitse — Fri, 18 Sep 2026 08:57:51 GMT

When you need to import an existing Power BI report and semantic model in Tenant B that's created in Tenant A, you need to jump through a number of hoops to fix the original connection. It's doable, but overly complex.

Why not, on import, show a pop-up that offers to change the connection in the PBIX file to one that is available in the Tenant B (or is created when importing the report). It will save time, and frustration.

Refill Buffer Error while refreshing a table

ShreyasMungad — Fri, 18 Sep 2026 08:53:28 GMT

The power bi has tables connected to Databricks SQL Server

While refreshing 1 of the tables getting this error: OLE DB or ODBC error: [DataSource.Error] ODBC: ERROR [08S01] [Microsoft][Hardy] (115) Connection failed with error: Could not refill buffer

I have already tried to reduce the size of the output by reducing the date range in SQL Query, cleared cache. The only step applied in power query is converting text to date for a date column. This error has started occuring after I replaced a column with another in Query.

The query loads correctly in Databricks, the power query preview loads as well, the problem occurs only after "Close & Apply"

New to this forum, I have tried looking for solutions on Copilot, nothing helps if any ideas please let me know, I will be very grateful

Enable per-user security for shared gateway connections using service accounts

AshishChougala — Fri, 18 Sep 2026 08:39:14 GMT

When a shared gateway connection uses a highly privileged service account, all source queries run with that account’s permissions. Users cannot see the stored credentials, but report developers who can use the connection may access any data available to the service account.

Currently, gateway permissions control who can use the connection, but cannot restrict source access by user, Microsoft Entra group, workspace, or semantic model.

Please add granular security controls that allow administrators to:

Restrict source objects by user, group, workspace, or artifact.
Approve which artifacts can use a shared connection.
Separate refresh permission from permission to create new connections or bindings.
Enforce the individual user’s source permissions for Import-mode development and refresh.

This would enable secure self-service reporting with centrally managed credentials, without requiring DirectQuery or multiple gateway connections.

Unifying two country BIs under RLS - how to handle historical data without a Country column?

ario0991 — Fri, 18 Sep 2026 07:10:28 GMT

Hi everyone,

We're working on a project to unify two separate Business Intelligence solutions (Spain and Italy) into a single one, using Row-Level Security (RLS) filtered on a Country field.

The RLS setup itself is clear to us. The issue is historical data: our old data doesn't have a Country column, since each BI used to be completely separate and never needed that distinction. Migrating/backfilling this historical data into the new unified model with a Country field is not an option for us.

One idea we had was adding buttons/links on the new reports that take users back to the legacy (old) reports for historical data, keeping the two worlds separate rather than merging them.

Has anyone dealt with a similar scenario, where historical data has to stay outside the new RLS-governed model? Specifically:- Is the "button linking to legacy report" approach a reasonable pattern, or are there better ways to handle this?- Any recommended navigation/UX patterns for pointing users from a unified report to a separate legacy report?- Any pitfalls we should watch out for with this kind of hybrid setup (new unified model + old standalone reports)?Thanks in advance for any input!

From Odoo & personal gateways to a Fabric Lakehouse: Medallion + Direct Lake, and F64 down to F16

psdhillon1 — Fri, 18 Sep 2026 06:49:02 GMT

We recently modernized the Power BI estate of a manufacturing client running Odoo while working on a project at DataToBiz

Before: Gen1 dataflows pulling Odoo over XML-RPC, Python scripts running through personal gateways, and many reports nobody opened. All of it ran on an F64 capacity with slow refreshes (trial version)

What we did (in about 60 working days):

Built everything in a separate dev workspace on a small F8 capacity, so production was never touched
Used report-usage metrics to pick only the reports worth migrating
Moved Gen1 → Dataflows Gen2, and switched Odoo XML-RPC → JSON-RPC
Moved the Python scripts into Fabric notebooks and removed the personal gateways
Bronze/Silver/Gold medallion into a Fabric Warehouse, then one certified Direct Lake semantic model
Rebound the Sales, Marketing and Production reports, ran UAT against the old workspace, then deleted the old content

Result: Production capacity went from F64 to F16 with stable CU usage. Estimated saving is about $75K per year. Refresh time went up significantly.

Question for the community:
1. When you downsize a capacity, how much CU headroom do you keep for spikes like month-end or bulk refreshes?
2. We used 75% peak utilisation on the Capacity Metrics app as our cut-off. Curious what thresholds others trust.

OData Connection Fails with Credentials Required Error in Power BI Service

Mohit009 — Fri, 18 Sep 2026 06:25:39 GMT

My Power BI report uses an OData feed from an internal application and refreshes fine in Power BI Desktop. After publishing to Power BI Service, the dataset refresh fails with the error:

"Credentials are required to connect to the OData source."
I already signed in to Power BI Service with the same account I use for the application, so I assumed it would use those credentials automatically. In the dataset settings, the OData source shows up but the status says credentials are missing. Is there an extra step I'm supposed to configure in the Service?

How to avoid view count on Audit Data of unauthorized person in Usage metrics

hariscool1103 — Fri, 18 Sep 2026 06:21:39 GMT

Hi My name is Haris,

I have a Power BI app link which I share with someone who does'nt have access to it.

But when he clicks the app link, the usage metrics or audit data still captures it has 1 view count since he clicked that link.

Since he does'nt have access to that app, the usage metrics should not include the count of that person who does'nt have access to it.

Please help me to relsolve this issue or help with the work around.

Thanks in advance

Optimising Fabric Capacity Usage

FabricEnjoyer — Fri, 18 Sep 2026 06:08:18 GMT

Hi all,

Due to the recent changes in Fabric capacity metering, I've been spending quite a bit of time trying to optimise our CU consumption. The challenge I've run into is that it's difficult to distinguish between SQL activity generated by user queries versus SQL activity generated during semantic model refreshes, which makes it harder to accurately evaluate different architectural approaches.

Approach 1: Materialising Views as Delta Tables

Historically, our semantic models have queried SQL views hosted in Fabric. To reduce SQL Endpoint consumption, I began converting the T-SQL view logic into notebooks that materialise the results into Delta tables. The semantic models then connect directly to these Delta tables via the ADLS Gen2 connector in Power BI, effectively bypassing the SQL endpoint during refresh.

However, the results have been inconsistent. In some workspaces this appears to reduce overall CU consumption, while in others it actually increases costs because the notebook execution itself incurs significant compute usage.

Current new architecture(old queried straight from omdb sql views):

My questions are:

From a CU consumption perspective, is it generally more efficient to:
- Have semantic models query Fabric SQL views directly, or
- Materialise those views into Delta tables and have semantic models consume the Delta tables through ADLS Gen2?
Has anyone compared the total cost of repeatedly querying views during semantic model refreshes versus the cost of creating and maintaining materialised Delta tables?
While I understand that a star schema is considered best practice for reporting and semantic modelling, if the same business logic can be represented through a set of SQL views, is there still a compelling reason to physically materialise the data into tables from a cost-efficiency perspective?

I've attempted to benchmark both approaches, but isolating the relevant consumption metrics has proven more difficult than expected.

Approach 2: Shifting Transformation Logic to the Semantic Model

Another approach I'm exploring is moving the transformation and view logic out of our Fabric workspace and into semantic models hosted in the client's Power BI Pro tenant.

My assumption is that this would reduce SQL Endpoint consumption within our Fabric capacity because less querying and transformation would occur on our side. However, I would expect semantic model refresh times to increase as more transformation work is pushed downstream.

My questions here are:

Would moving the transformation logic into the semantic model generally reduce or increase overall SQL-related CU consumption?
Have others seen meaningful capacity savings using this approach?
Is there a way to connect one semantic model to another semantic model without using DirectQuery? My concern is that DirectQuery would negatively impact performance and potentially introduce additional query-related costs.

I'd be very interested to hear how others are approaching this, particularly now that SQL Endpoint consumption has become much more visible within Fabric capacity metrics.

I have also reviewed the Query Insights for each workspace in an attempt to correlate SQL activity with overall CU consumption. However, this does not always translate into lower capacity usage. One of the challenges is that many users are querying the SQL Endpoint directly through Excel and other tools, without going through a semantic model at all. Under the new metering model, these ad hoc user queries appear to be a significant contributor to capacity consumption, making it difficult to isolate the impact of semantic model refreshes versus interactive user activity. As a result, determining whether a particular optimisation has genuinely reduced costs becomes far more challenging, as overall CU usage may be heavily influenced by user behaviour outside of the reporting layer.

Thanks in advance!

Sales forecast based on historical data

eliseglines — Fri, 18 Sep 2026 05:57:01 GMT

I'm having some trouble writing an equation that I think should be simple and doable.

I'm looking at sales data for many different companies- I have the total amount of sales as well as the exact date the amount was paid. I have written equations to move this exact date into a month, quarter, and year. I want to be able to look at the average sales amount (filtered by companies in Power BI) across time and use this historical data to project future sales.

Average of most recent 3 quarters of sales data- look at growth between these quarters, average this growth, and use that average growth to the most recent quarter's sales amount as a projection for the next quarter.

Everything I've tried ends up in an error message - hoping there is someone out there who can help point me in the right direction.

Overrule Calendar tabel connection

MaleneL — Fri, 18 Sep 2026 05:54:26 GMT

Hi community

I need two make a measure that sum the payments with at start date >= “this month” (end of month) and an end date <= “this month” (end of month). This part is okay.

But the trick is to overrule the date.

Date end date start date payment

31-03-2026 31-12-2026 26-01-2026 118

31-03-2026 31-12-2026 19-01-2026 113

31-03-2026 31-12-2026 16-01-2026 232

28-02-2026 31-12-2026 09-01-2026 242

31-01-2026 31-12-2026 01-01-2026 287

31-01-2026 31-12-2026 01-01-2026 34

31-01-2026 31-12-2026 01-01-2026 135

31-01-2026 31-12-2026 01-01-2026 97

31-01-2026 31-12-2026 01-01-2026 225

31-01-2026 31-12-2026 01-01-2026 147

31-01-2026 31-12-2026 01-01-2026 496

31-01-2026 31-12-2026 01-01-2026 334

31-01-2026 31-12-2026 01-01-2026 408

28-02-2026 30-11-2026 01-01-2026 83

31-12-2025 30-11-2026 01-12-2025 253

31-12-2025 30-11-2026 01-12-2025 179

31-12-2025 30-11-2026 01-12-2025 97

31-12-2025 30-11-2026 01-12-2025 96

31-12-2025 30-11-2026 01-12-2025 332

31-12-2025 30-11-2026 01-12-2025 357

31-12-2025 30-11-2026 01-12-2025 157

31-12-2025 30-11-2026 01-12-2025 120

31-12-2025 30-11-2026 01-12-2025 33

Date is my “datekey” – the one that tells what month to normally to put in the payment. I need to overrule this and use the one that is paid in the interval. I have built this DAX meauser:

VAR _Day = Max [tabel[Date])

RETURN

CALCULATE(

SUM(Tabel[Payment]),

Fact_Privatsikring[betaling_start_dato] <= _Day &&

Fact_Privatsikring[betaling_slut_dato] >= _Day)

So, for the list that means the total 4.670, but do to my “dateKey” (wish cannot be remove, I need that for all the other measurement) it will only take the on from 31-01-2026 (the one in bold).

So, the question is how do I overrule the datekey for this specific measure so I for January gets the payments in the interval >= start date (31-01-2026) and <= end date (31-01-2026) for each month?

Power BI - incorrect handling of empty string parameter in DAX query in paginated report

roshinikumarkar — Fri, 18 Sep 2026 03:44:45 GMT

Blank string parameters in paginated reports are treated as empty strings in Power BI Report Builder but as null values in the Power BI Service, resulting in incorrect report output.

-> Create a paginated report.
-> Create a string parameter that:

Allows blank
Does not allow null.

Expects both environments to behave the same way. Since the parameter is configured to allow blanks but not nulls, expect the Power BI Service to pass an empty string ("") rather than NULL.

Offline Meetup in october

Praful_Potphode — Fri, 18 Sep 2026 03:08:09 GMT

Hi All,

We are planning an pune offline meetup in october month.

Anyone interested in organizing it can dm me separately

Regards,

Praful