rss.livelink.threads-in-node

Distance-Based Location Highlighting

Swami_om — Fri, 18 Sep 2026 15:28:14 GMT

Hello,

I’m trying to calculate the distance between the assigned location and the employee’s home location using the latitude and longitude available in this file.

The objective is to identify locations that are within the selected distance range (in kilometres) using a kilometre slicer. I would like the locations that fall within the selected range to be highlighted in green, while all other locations remain grey.

Additionally, I would like the selected location to be highlighted based on the selected City, Employee Location, or StoresDummy[City] value.

I’ve also referred to the following YouTube video for guidance: youtube

Could you please help me implement this logic in Power BI/DAX?

Sample data

Best Practices for Handling Incremental Data Loads in Dataflow Gen2

codeautomation — Fri, 18 Sep 2026 15:26:56 GMT

Hi everyone,

I am exploring different approaches for handling incremental data loads with Dataflow Gen2 in Microsoft Fabric.

For larger datasets, refreshing the entire dataset every time can become inefficient, so I am interested in understanding how others are designing their dataflows to process only new or changed records.

A few questions:

How are you identifying and filtering changed records between dataflow runs?

Is it better to manage incremental logic directly inside Dataflow Gen2, or use a Fabric pipeline to control the process?

How do you handle failed runs or partially processed data without creating duplicate records?

Are there any recommended patterns for maintaining good performance as the volume of historical data grows?

I would appreciate hearing about approaches that have worked well in real Fabric environments.

Designing AI Agent Workflows on Modern Data Platforms

codeautomation — Fri, 18 Sep 2026 15:21:22 GMT

Hi everyone,

I have been exploring how AI agents can work with modern data engineering platforms to automate business processes.

A common architecture I see is that data pipelines collect and prepare information, AI agents analyze the context and determine the next action, workflow services execute tasks through APIs or connected systems, and the results are stored for reporting or further processing.

I am interested in how teams are approaching this with Microsoft Fabric.

Are you using AI agents directly with Fabric workflows, or keeping the AI layer separate?

What patterns work well for connecting AI agents with Lakehouse, Data Pipelines, or notebooks?

How do you manage security and permissions when AI applications need access to enterprise data?

Are there recommended approaches for combining Fabric workloads with external AI services?

I would be interested to hear what architecture patterns others are using and what challenges you have encountered.

The permissions granted to user '' are insufficient

eduardserediuc — Fri, 18 Sep 2026 15:18:41 GMT

I installed Power BI Report Server – SQL Server 2025 Standard, product version 15.0.1121.109, on the primary node only of a Windows Failover Cluster SQL Server 2025 (no AG) – OS Windows Server 2025 Datacenter.
I configured the SMTP settings in the Email Settings section of the Report Server Wizard.
I created a shared PostgreSQL data source that connects using a username and password, and then created a paginated report using that data source.
The permissions for my AD user on the Report Server folder containing both the data source and report are all: Browser, Content Manager, My Reports, Publisher, Report Builder.
I created an Email distribution subscription for my report, but when I run the subscription either manually or on schedule I get the below error:

Failure sending mail: The permissions granted to user '' are insufficient for performing this operation.Mail will not be resent.

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.

Manage Roles - Row level data restrictions

Msampedro — Fri, 18 Sep 2026 14:10:12 GMT

Hi all,

Working on row level data restrictions. I do have a Budget table by Sales representative. Every sales person only can see their own data, plus two managers that they can see the sales representatives reporting to them and finally Director having access to all the data. Somehow it's not working.

I have created a "Security Table" with the users and Sales Rep ID, what indicates which users can see each one (i.e.; meaning Director can see Sales Rep ID 1,2,3 & 4. Manager1 sales rep ID 1 and 2, Manager2 sales rep ID 3 and 4 and finally Sales rep1 only can see Sales rep ID 1 and so on...

Model view below with Relationships are both one to many.

Formula DAX use in the manage roles

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