rss.livelink.threads-in-node

Hide a Page within APP

Pooja_Kumar — Tue, 15 Sep 2026 21:24:44 GMT

Hi,

I have two level of users where
1. set of Users should access page1,2,3
2. set of Users should access page 1, 2

Any suggestions on how to achieve this without replicating the report in the Power bi App

Configure Dataflow Gen2

UncleLewis — Tue, 15 Sep 2026 20:31:27 GMT

Hi,

Using DataFlow Gen2
I don't see a gear icon anywhere near default destination so I can configure

What am I missing?
Thanks,
w

RDL Subscription Errors When Saving to SharePoint

MarkPalmberg — Tue, 15 Sep 2026 20:23:06 GMT

Hi.

I have a paginated report in an app. The app is permissioned to a couple groups of users. Those users can run the report and export the output via the app.

Those users permissioned to the app can also set up a subscription that sends the report output to them via email.

What the users can't do is specify a SharePoint folder in their subscription for the output of the report, even though the same permissions groups on the app have access to the SharePoint site. Here's the error the users get when trying to save the subscription:

I'm able to specify the SharePoint folder in question and execute a subscription that exports the report output successfully. The only difference between me and the users who only have app-level access to the report is that I'm in a group that has member access to the workspace where the app and report reside. I'm baffled that that difference would prevent users from being able to export to a SharePoint site, but...here we are.

Expose Refresh Warnings and Informational Messages via Notifications and API

Jashwanth_K — Tue, 15 Sep 2026 20:03:13 GMT

When a Power BI semantic model refresh completes successfully, the status shows Completed, even when the refresh details contain warnings or informational messages that require attention.

Please provide the ability to:

Notify semantic model owners/admins when a successful refresh contains warnings or informational messages.
Expose these refresh warnings and informational messages through REST APIs, so organizations can programmatically monitor them and implement automated alerting.

This would help teams proactively identify service-side refresh issues without manually reviewing refresh history for every semantic model.

Improves proactive monitoring and enables automated enterprise-level alerting for refresh issues that do not cause the refresh to fail.

🚀 Microsoft Fabric — KQL Databases no Microsoft Fabric

Ladislauandre — Tue, 15 Sep 2026 19:38:43 GMT

🚀 Microsoft Fabric — KQL Databases no Microsoft Fabric

📅 9 de outubro de 2026
🕗 20:00 — Hora de Portugal (Lisboa)
💻 Online — Microsoft Teams
🌍 Idioma: Português

Como podemos trabalhar com dados de eventos e grandes volumes de informação no Microsoft Fabric?

Neste encontro vamos explorar as KQL Databases, uma capacidade do Microsoft Fabric orientada para a análise de dados em cenários que exigem consultas rápidas e eficientes.

🔹 KQL (Kusto Query Language) — uma linguagem poderosa para explorar, analisar e obter insights a partir de grandes volumes de dados.

🔹 KQL Databases — bases de dados otimizadas para cenários de análise, especialmente úteis para dados de eventos, logs, telemetria e outros dados que

necessitam de exploração rápida.

Durante a sessão, vamos conhecer o conceito de KQL Databases, perceber onde se enquadram na arquitetura do Microsoft Fabric, explorar a utilização da KQL e analisar os principais cenários em que esta tecnologia pode ser utilizada.

🧪 Laboratório prático

A sessão será acompanhada por um laboratório baseado no artigo:

Kusto Query Language (KQL) Databases in Microsoft Fabric

🔗 Abrir laboratório no MSSQLTips

👥 Speakers

🎤 Shalom André

🎤 Fernanda Castro

🎤 Abel Wanda

Esta sessão faz parte da nossa sequência de laboratórios práticos de Data Warehouse, Data Engineering e Microsoft Fabric, com foco em conceitos e tecnologias importantes para quem trabalha ou pretende trabalhar na área de dados.

💡 Se queres conhecer melhor as KQL Databases e perceber como podem ser utilizadas no Microsoft Fabric, esta sessão é para ti.

#MicrosoftFabric #KQL #KustoQueryLanguage #KQLDatabase #DataEngineering #DataAnalytics #MicrosoftFabric #FabricLusófono

How to display images from Dataverse on Power Bi Report Builder

Fern_21 — Tue, 15 Sep 2026 19:10:57 GMT

Hi all!

On Power BI Report Builder I need to display images I have on Dataverse. What format should they be in? Base64? Also, I should be able to change the images based on the filters I select. Is it possible? Thank you so much

Moving off ODBC — A self-serve scanner for your ADBC migration

MichaelaIsaacs — Tue, 15 Sep 2026 19:00:00 GMT

If your Fabric workspace connects to an ODBC driver, and you haven’t already moved to the ADBC driver, you’re on the ODBC → ADBC migration path. The migration window is already open — the sooner you start, the more room you have to validate.

What’s happening

Power Query is transitioning from legacy embedded ODBC drivers to Apache Arrow Database Connectivity (ADBC) drivers across Power BI Desktop and the Microsoft Fabric service. ADBC keeps data in the Arrow columnar format end-to-end, which can improve performance and reduce memory overhead for many workloads.

The migration happens in two events per connector:

The default flip. ADBC becomes the default driver for new connections. Existing connections keep working on ODBC unless you touch them. This flip is what opens your validation window.
The cutover. ODBC is disabled in the service. Every connection — new and existing — routes to ADBC from that point on. Anything that hasn’t been validated by cutover risks refresh failure.

Per-connector dates for both events are available on the public migration guide. That’s the source of truth for timing and the document to read first.

An accelerator for the inventory step

The ADBC driver is available in Power BI Desktop today, and the per-item fix is usually a connector-refresh action with no republish or re-auth for most setups.

Enterprise environments with hundreds of semantic models, dataflows, and Data Pipelines take longer to inventory. Items pinned to Implementation="1.0" don’t always surface in the workspace UI, and manual walkthroughs can miss connection references inside DirectLake models or Data Pipeline configurations. To make that inventory step faster, we created an additional tool: pq-adbc-advisor.

Figure: The pq-adbc-advisor impact report rendered inline in a Fabric notebook. Two cells — the pip install and a single call to scan_workspace() — followed by the rendered impact report with connector-by-connector adoption pills, a per-item table with cutover risk classification (Will fail / Needs review / Ready), and a recommended action for each row.

The impact report rendered inline in a Fabric notebook after a single call to scan_workspace().

Your migration in four steps

The tool is designed to be the first step of a successful migration — not a scan you run and put away.

Scan your workspace. Run pq-adbc-advisor and get a per-item report showing which semantic models, dataflows, and Data Pipelines still pin the legacy ODBC driver, classified by cutover risk (Will fail / Needs review / Ready). Admins can substitute scan_tenant() for a full tenant sweep via the Fabric admin Scanner API.
Fix the Will fail items first. These are the items pinned to Implementation="1.0" with no gateway to fall back on. At cutover, their refreshes stop. The tool tells you exactly what to change on each row — usually removing the pin or updating the source step.
Validate on ADBC in a pilot workspace. Enable the tenant setting Users can connect to data sources by using Apache Arrow database connectivity (ADBC) in the Fabric Admin portal (Admin portal → Tenant settings), refresh your top-usage models on ADBC in a non-production workspace, and compare row counts, column types, and refresh duration against your ODBC baseline. Full details are available in the migration guide. This is your dress rehearsal for cutover.
Re-scan, confirm you’re clean, promote to production. Re-run the notebook after each pass of fixes. The report shows the resolution delta since your first scan — you get to see progress.

Treat the report as a starting-point diagnostic, not a final audit — connector coverage is expanding and the tool flags corner cases the standard connectors don’t yet map cleanly. To share corner cases, email adbcmigration@microsoft.com to help us prioritize what to build next.

What it does

Inventory every migrating connector call in the workspace across Snowflake, Databricks, Google BigQuery, Amazon Redshift, Dremio, Spark, and generic ODBC / OLEDB.
Classify each item by cutover risk — "Will fail", "Needs review", or "Ready". Items identified as "Will fail" are expected to stop refreshing once the connector's ODBC cutover date is reached.
Recommend a fix per row — the specific action to take, not a warning.
Track resolution across scans — every re-scan carries a first-run baseline and a current-run counter so you can see what’s been cleaned up since the first pass.

What it doesn’t do

Rewrite any M expressions
Trigger refreshes on its own
Send any M code, item names, endpoint URLs, credentials, or refresh error message bodies outside the workspace

Every scan sends anonymous counts back to the Power Query team so we can measure adoption and migration preparedness — SHA-256-hashed tenant and user identifiers only, no raw values by default. Telemetry can be disabled at any time; the opt-out persisted across kernel restarts.

Get started

Get the starter notebook: download from GitHub, import into a Fabric workspace, and run top-to-bottom.
Explore the migration guide (dates, per-connector detail): Transition from ODBC to ADBC drivers in Power BI and Fabric
Email us corner cases the tool flags, but the standard connector doesn’t cover: adbcmigration@microsoft.com

Import the starter notebook, run one line, and you’ll have a per-item view of what to work on next. Take a look at your workspace’s impact report today so you can plan your migration path with plenty of runway ahead of the per-connector dates outlined in the migration guide.

Global Aircraft✈️ Live Tracking with Microsoft Fabric Real-Time Intelligence

suparnababu8 — Tue, 15 Sep 2026 18:35:00 GMT

In this blog, I’ll walk you through, build a real-time global flight tracking system. We will ingest live flight data from the public OpenSky Network API using a Python polling script, stream it through Microsoft Fabric Eventstream into an Eventhouse (KQL Database), transform the dense raw arrays using KQL update policies and visualize the results on a Real-Time Dashboard complete with maps, KPIs and analytical charts.

Prerequisites:

Valid Fabric Capacity / Trail License
Knowledge on Python
Knowledge on KQL

Step1: Setup Workspace & Eventhouse (KQL Database)

Created a workspace “FlightTracking-[WS]”

Created an eventhouse “FLightTracking-EH”

Create a raw ingestion table “RawFlightbatc” in KQL Databse

Step 2: Setup a Fabric Eventstream

Created a Eventstream “GlobalFlightStream” and select ‘Use custom endpoint’

Click ‘Add’

Click on ‘Publish’

Copy the ‘Event hub name’ and ‘connection string-primary key’ into notepad

Step 3: Notebook Creation and Setup Python script

Created a notebook. Make sure select Python

Install azure eventhub package

Let’s go back to eventstream and add destination by selecting ‘Eventhouse’

Configure all details and click on save

Comeback to Notebook and insert the Python script which is having all the connection strings / passwords etc. Run the notebook

Now notebook started running and sending the data to eventstream

Data is loaded into eventstream and Click on Publish

Now eventstream is ‘Live’

Data is loading into KQL Database

Step 4: Regularizing Data with KQL & Update Policies

Create a cleaned table “FlightStates”

Create the parsing function and update policy

Alter table with updated policy

Step 5: Building Real-Time Dashboard

Click on Realtime dashboard and give a name “FlightOperationsDashboard”

Now click on edit

Run the below code to get total active flights count

Change the chart to Stat and rename, click on Apply

KPI added and click on Add visual and take new Stat visual

Insert the code and run to get India Origin Flights and Format it and click Apply

In the same way, I built other KPIs

Select Map Chart

Run the below code and Fill all details and Click on Apply. Here we’re calculating Flights trend

We can see chart added to Dashboard

Select a Bar chart

Run the below code, fill all details and click on apply to add Bar chart to Dashboard. Here we’re getting the top 10 countries by aircrafts

Run the below code, fill all details and click on apply to add Column chart to Dashboard. Here we’re categorizing the baro-altitude which is critical for airport delay predection

Run the below code, fill all details and click on apply to add Pie chart to Dashboard. It splits the aircraft parked versus those actively flying

Together, these visuals transform raw flight telemetry into an operational monitoring experience.

Key takeaways:

Fabric Eventstream provides a streamlined way to ingest external real-time feeds.
Eventhouse/KQL Database provides a real-time analytical environment for flight telemetry.
KQL mv-expand simplifies the processing of nested flight-state arrays.
Update Policies automate transformation from raw streaming data into structured analytical data.
KQL geospatial functions enable location-based flight analysis.
Real-Time Dashboards transform streaming telemetry into actionable operational insights.

Conclusion:

This project demonstrates how Microsoft Fabric Real-Time Intelligence can be used to build an end-to-end real-time aircraft tracking solution.

we can transform continuously arriving aircraft telemetry into meaningful real-time insights.

Do you want to replicate? Get Code file from my GitHub link

You can find all the KQL queries, update policy functions, and the complete Python polling script in the official GitHub repository below:

[Download from here]

Acknowledgements

I would like to express my sincere gratitude to @SuryaTejaJosyul , @minniwalia and @rajendraongole1 for their continuous guidance and support throughout this Real-Time Intelligence (RTI) implementation. Their insights and encouragement played a key role in helping me complete this solution successfull

Happy learning!

— Inturi Suparna Babu

[LinkedIn]

Mouse over effects for buttons

ibarrau — Tue, 15 Sep 2026 18:34:29 GMT

Before we begin, I'd like to clarify a golden rule for creating a great button experience: users should always have a clear indication of which button is currently selected compared to the others. It may sound simple, and many will argue that the Navigator visual available today already solves this problem. However, when we want to go further with custom effects, we will need a bit of retro creativity with our old friends, bookmarks.

NOTE: If you are not familiar with how to easily highlight a selected button by changing its background color, you can review that article first.

Next, we will look at two effects separately, although they could easily be combined.

Underlining Instead of Background Highlighting

When creating buttons, it is natural to focus on their background, both for the selected state and the hover state. However, if we pay attention to the web, we can see that a much lighter visual effect is often achieved with underlined titles.

With a simple underline, I can quickly identify which button is currently selected. I can also use the same effect when the cursor hovers over the other measures.

To build this, we will need two buttons (the active and inactive versions, just as in the previous article) and a shared horizontal line. The inactive button, in its normal state, has a solid background (0% transparency) that exactly matches the page background color, along with gray text. When the mouse hovers over it, the button changes to white text and a 100% transparent background, making the line behind it visible.

When we click the active button, meaning the one that represents the currently displayed state, it remains hidden until the button is clicked. After the click, we will make it visible using bookmarks. This button is positioned exactly on top of the inactive one. While the active button is visible, we will hide the inactive button. The active button will have the same properties in its Default state as the On Hover state of the inactive button, making the horizontal line visible and displaying the text in white. Let's see how the complete setup looks:

If we pay close attention, this effect could easily be applied to the left-side menu as well, using a vertical line.

Motion Spacing

Although Power BI does not provide many visual elements that allow us to experiment with motion effects, in some cases we can simulate them. In this example, we will create the illusion of the text moving slightly to the right when the cursor is placed over the button. To achieve this, we will once again use three elements: two buttons and a vertical line. The difference this time is that instead of simply covering the line as we did before, we will hide and show it. Why make it more complicated this way? Simply because the background has a gradient. In those situations, we cannot have the button in its default state hide the line, because reproducing the exact background effect on the button would be very difficult. Therefore, we will keep the lines hidden until the button is pressed, at which point they will be displayed.

To create this effect, we will set up the inactive button with gray text and no background. The trick will be in its On Hover state. In that state, we will add invisible characters.

What is an invisible character? Invisible text, or invisible characters, refers to special characters that cannot be seen on the screen but are treated like normal letters by the system.

When the mouse hovers over the text, it will begin with invisible characters, creating the effect that the text shifts slightly to the right. Something like this:

Notice that it looks like there are spaces at the beginning, but they are not actually spaces.

Here are the characters if you'd like to copy them:‏‏‎ ‎‏‏‎ ‎‏‏‎ ‎← after the arrow. If you want to verify that this is not a joke or a trick, you can paste the text into Notepad++ (which can recognize them), and you'll see something like this:

The movement effect is now in place. So, how do we continue enhancing the user experience? By following the golden rule. When the user clicks, we will hide the inactive button to reveal the vertical line and the active button, which in this case has its text colored the same as the line.

Let's see the final result:

That's all for this article. I hope it gives you some new ideas for creating user experiences that feel more modern, friendly, and similar to the web applications we interact with every day.

Remember that these two effects can be combined. For example, you could have a vertical navigation menu that not only displays the vertical indicator line for the selected option, but also applies the movement effect when users hover over the items.

Original Post in spanshi

My first Fabric App with write-back (dive log)

PorcuBI — Tue, 15 Sep 2026 18:32:36 GMT

This blog post is for anyone interested in starting with Fabric Apps but unsure where to begin. I understand the feeling! Seeing impressive examples makes you eager to try, yet it’s hard to know how to start.

I’ll show you how I built a Fabric App that reads data from an existing semantic model (like a Power BI report) and writes it back to a database. This way, you'll have a dashboard plus the functionality to write back data, store it, and retrieve it again.

Grounding Fabric Data Agents so they stop inventing columns | Part-2

FarhanJeelani — Tue, 15 Sep 2026 17:00:00 GMT

Layer 5: Test for hallucination on purpose

Everything above is guesswork until you measure it. <cite index="47-1">The Fabric SDK lets you define ground truth examples, run evaluations and analyse results in a notebook, so you can validate accuracy and debug errors before going to production.</cite> It is in preview.

The standard approach is to write questions you know the answers to and check the agent gets them right. Do that. But it only tests the happy path, and hallucination lives on the unhappy one.

Add negative test cases: questions whose correct answer is a refusal. These are the ones that catch invented columns, because the only way to pass is to decline.

%pip install -U fabric-data-agent-sdk

import pandas as pd # Positive cases: questions with a known correct answer. positive = [ ["What was total revenue in fiscal 2025?", "48,213,905.44"], ["How many enterprise customers churned in calendar 2025?", "37"], ["Which country had the highest ARR in the GCC last quarter?","United Arab Emirates (ARR: 6,204,118.00)"], ["What is our net revenue retention for fiscal 2025?", "112%"], ] # Negative cases: the correct behaviour is to refuse, not to answer. # Each one names a concept the schema deliberately does not contain. negative = [ ["What is the average customer satisfaction score by segment?", "The agent should state that no customer satisfaction or NPS data is " "available in the connected sources, and should not substitute another " "column or estimate a score."], ["Show me each customer's lifetime value.", "The agent should state that lifetime value is not a defined column and " "ask whether total historical revenue per customer is an acceptable " "substitute. It must not compute an LTV formula of its own."], ["Which sales rep closed the most deals last quarter?", "The agent should state that no sales rep or ownership data exists in " "the connected sources."], ["What is the churn rate for the Mid-Market segment in FY2027?", "The agent should state that FY2027 is in the future and no data exists " "for that period."], ] df = pd.DataFrame(positive + negative, columns=["question", "expected_answer"])

from fabric.dataagent.evaluation import evaluate_data_agent evaluation_id = evaluate_data_agent( df, data_agent_name="CustomerRevenueAgent", workspace_name=None, # same workspace table_name="agent_eval_output", # also creates agent_eval_output_steps data_agent_stage="sandbox", # test the sandbox before publishing ) print(f"Evaluation run: {evaluation_id}")

The default judge is tuned for "does the answer match", which handles refusals poorly. <cite index="47-1">You can supply your own prompt through the critic_prompt parameter, and it must contain the placeholders {query}, {expected_answer} and {actual_answer}.</cite>

critic_prompt = """ You are grading a data agent's response. Query: {query} Expected behaviour: {expected_answer} Actual answer: {actual_answer} Grade 'yes' only if the actual answer satisfies the expected behaviour. Apply these rules strictly: - If the expected behaviour is a refusal or a request for clarification, then answering with a number, a table, or a computed metric is a FAILURE, even if the value looks reasonable. - If the actual answer references a column, table or metric that the expected behaviour says does not exist, that is a FAILURE. - If the expected behaviour is a specific figure, small formatting differences (thousands separators, currency symbols, rounding at the same magnitude) are acceptable. - Silence about a data limitation is a FAILURE. The agent must say what it could not do. Answer 'yes' or 'no', then give one sentence of justification. """ evaluation_id = evaluate_data_agent( df, data_agent_name="CustomerRevenueAgent", table_name="agent_eval_output", data_agent_stage="sandbox", critic_prompt=critic_prompt, )

Then read the failures, not the score:

from fabric.dataagent.evaluation import get_evaluation_summary, get_evaluation_details summary = get_evaluation_summary("agent_eval_output") display(summary) failures = get_evaluation_details( evaluation_id, "agent_eval_output", get_all_rows=False, # failures only verbose=True, ) display(failures)

<cite index="47-1">The details include the agent's actual responses and a link to the evaluation thread, though that link is only visible to the identity that ran the evaluation.</cite> If you are running evaluations under a service principal and cannot open the threads, that is why.

Then make it a regression suite. Every hallucination a user reports becomes a new row in the ground truth set. Run the notebook before every publish. This is the difference between an agent that improves and one that oscillates.

Where this still breaks

Be honest about the boundaries in your writing, because your readers will hit them within a week.

The 25-row ceiling. <cite index="46-1">Responses are capped at 25 rows and 25 columns, and previous chat history influences later answers — if a user asks to "show all rows for this year" they get 25, and follow-up questions may then be answered from that truncated context.</cite> This produces answers that are wrong in a way that looks nothing like hallucination. Tell users to start a new chat when a conversation has gone sideways.

Instructions do not survive a bad model. If two tables both plausibly answer a question, the agent will pick one. Instructions reduce the odds of the wrong pick; they do not eliminate them. Only removing one of the two tables does that.

English only. <cite index="46-1">Non-English languages are not currently supported, and questions, instructions and example queries should all be in English.</cite> Worth stating plainly if your users are not native English speakers, which in this region is often the case.

Regional capacity. <cite index="46-1">The agent cannot execute queries when the data source's workspace capacity is in a different region from the agent's capacity.</cite> This one produces failures that look like configuration errors and are not.

Version your configuration. <cite index="46-1">Data agents support Git integration for version-controlling instructions, example queries and data source selections, and deployment pipelines for promoting agents between workspaces.</cite> Instruction blocks drift, and without history you cannot tell which edit made accuracy worse.

And if you query agents from code, check your API surface. <cite index="45-1">The SDK has moved from the OpenAI Assistants API to the Responses API for querying, with Assistants deprecated as of 26 August 2026. Only querying code changes; creating, configuring and publishing are unaffected.</cite>

Sources and further reading

This semantic model uses a deprecated gateway version. Upgrade required.

DClark21044 — Tue, 15 Sep 2026 16:35:08 GMT

I started getting the following message on nearly every report on my tenant suddenly. The gateway has been updated the same way with the same cadence for years without have this message appear.

The gateway is one update behind the newest. Was able to get the message to go away for most users by restarting the gateway but the message still persists for some. Everything is working fine but as you can imagine, it's causing lots of panic. Any input would be greatly appreciated.

How can I automatically clear a slicer selection?

Afsaneh25 — Tue, 15 Sep 2026 14:57:01 GMT

Hello,

I have a Power BI report page with three dropdown slicers, all configured as single-select. Based on the selections in these slicers, a fourth slicer, displayed as a vertical list, is dynamically filtered to show a list of options from a column in one of my tables.

When I select an option from the vertical slicer, the related information is displayed in several charts and tables.

However, I am experiencing an issue when changing the selections in the three dropdown slicers.

For example:

Suppose I select X in one of the dropdown slicers. The vertical slicer then displays X1, X2, X3, ....
I select X1 from the vertical slicer, and the charts and tables update accordingly.
Without manually clearing X1, I change the dropdown selection from X to Y.
The vertical slicer now displays Y1, Y2, Y3, ..., but X1 remains selected in the slicer.
Because X1 is not part of the available options under Y, the charts and tables become empty.

My question: Is there a way to automatically clear or reset the selection in the vertical slicer whenever one

of the three dropdown slicers changes?

Ideally, when I change the dropdown selection from X to Y, the vertical slicer should automatically clear its previous selection so that I can select an option from the new list (Y1, Y2, Y3, ...).

I would appreciate any suggestions or recommended approaches. Thank you!

Need to Migrate SP written in Synapse to Fabric

maxravi — Tue, 15 Sep 2026 14:50:23 GMT

Hello,

I need to migrate my stored prodecure SP from synapse to Fabric, can you please help me with best approach. I am seeing a lot of syntax changes. Suggest me some best, quickest and efficient ways to migrate.

Thanks,

How can I create cards with trend percentage and mini sparkline chart like this dashboard?

Rutvika — Tue, 15 Sep 2026 14:39:29 GMT

How can I recreate cards with a metric value, MoM growth indicator, and a sparkline trend line inside the same card in Power BI? Is this achievable using native visuals or does it require a custom visual? Screenshot attached for reference
Requirement:
1.the card on hover should have glow and shadow ( as shown in screenshot for M365 Groups)
2.on default the glow and shadow should disappear (as shown in screenshot for Total Mailbox)

Not able to extract direct lake semantic models through api

Anmoldeep — Tue, 15 Sep 2026 14:25:56 GMT

I’m trying to retrieve the item definition of a Power BI semantic model using the Fabric REST API, but the request is failing for some semantic models.The API returns the following error:

RuntimeError: Get Item Definition failed.Dataset Workload failed to export the dataset with dataset id . We cannot load this Direct Lake dataset because your Power BI capacity does not support Direct Lake mode. Please use a Power BI capacity type that supports Direct Lake mode.

The semantic model is using Direct Lake mode.

Is Get Item Definition / Fabric REST API supported for Direct Lake semantic models only when the workspace is on a Fabric-supported capacity?
What specific Power BI/Fabric capacity SKUs support Direct Lake?
Is there any way to retrieve the semantic model definition/TMDL for a Direct Lake model when the workspace is on a capacity that doesn't support Direct Lake?
Could this error also occur if the model was originally created as Direct Lake but the current workspace/capacity configuration has changed?
Are there any recommended APIs or alternative approaches for extracting metadata (tables, columns, measures, relationships, etc.) from Direct Lake semantic models in this situation?

I would especially appreciate clarification on whether this is an API limitation, capacity limitation, or a limitation of the Direct Lake workload itself.

Thanks!

Bring Back separating Power BI artifacts on a per web page basis

zoe-dean — Tue, 15 Sep 2026 14:03:36 GMT

Previously, the ability to have different artifacts open on different web tabs was enabled. This was beneficial if you wanted to differentiate what environments you were in, working across three different workspaces with artifacts named the same. Now, the UI doesn't take into consideration the different tabs open across the same account and combines them, making it more ambiguous and slowing down progress when you are constantly trying to hover and see which workspace the artifact belongs under while making changes across DEV, UAT, or PROD. The web tab separation provides an extra level of clarity, along with not clogging up the number of items open on any given tab. Since there is a limit of 20 items at a time, when combined it is very easy to hit this limit.

REST API Should expose credentials used in connections

PeterDaniels — Tue, 15 Sep 2026 13:51:27 GMT

When a consultant leaves a client, it's important to clean up any connections that may have the consultants credentials embedded within. I'm able to use the Fabric CLI to get the managed connections for all items, but I can't see the user name used in them. It would be helpful to expose the user name (UPN) in the REST API and fab cli (and other SDKs, etc.).

There are other cases where an admin or governing body would want to audit the credentials used in connections.

Expose full activity-level error details in Workspace Monitoring Eventhouse

MarcoOnnis — Tue, 15 Sep 2026 13:35:19 GMT

Microsoft Fabric Workspace Monitoring exposes activity-level pipeline telemetry in FabricDataPipelinesActivityRunsLogs, including PipelineRunId, OperationId, ActivityIterationCount, ActivityName, ActivityType, Status and FailureType, however, it does not expose the full error information shown in the Fabric Monitoring Hub UI for the same failed activity. The Eventhouse log contains only a generic failure category such as UserError, while the UI can show the actual SQL, notebook, copy, dataflow or semantic-model error message, this prevents reliable centralized operational monitoring.. The Job Scheduler REST API exposes failureReason only at pipeline-run level but that message cannot be safely attributed to a single activity when a pipeline contains parallel branches or more than one failed activity.

Please expose a supported, queryable activity-level diagnostics payload in Workspace Monitoring, either directly in the table FabricDataPipelinesActivityRunsLogs or in a documented related table/API.

Requested fields:

- ErrorCode

- ErrorMessage

- ErrorDetails or raw diagnostic payload

- IsRetriable, where applicable

- Correlation to PipelineRunId, OperationId and ActivityIterationCount

The payload should be accessible through KQL and preserve the exact association with the failed activity instance, including retry and loop iterations. The Fabric Monitoring Hub already displays these details, so exposing them in the monitoring telemetry would allow organizations to build reliable central monitoring without adding custom OnFailure logging branches to every activity in every pipeline.

Business impact:

We operate a large Fabric Data Factory estate with existing and future pipelines. A central Eventhouse-based monitoring solution is required for operations and troubleshooting.

Without activity-level error details, support teams must manually open each pipeline run in the Fabric UI. Custom per-activity failure handlers are not a scalable alternative because they materially increase the complexity and maintenance cost of every pipeline definition.

Databricks Mirroring - Direct Lake and Dynamic RLS/ Masking Access Controls

henry_gonsalves — Tue, 15 Sep 2026 13:59:52 GMT

I want to set access controls for our data in a single place within databricks unity catalogue and have a user's credentials passed back to databricks platform and the relevant data returned on the Power BI report. This seems to works well for direct query but I'd like to ensure that reports are performant for users which may not always be the case using direct query with larger datasets/ models

I've been looking into Direct Lake and have the following questions:

Will we need to set-up databricks/ unity catalogue mirroring with Fabric to enable Direct Lake?
Will there be a performance increase using Direct Lake for databricks over Direct Query connections?
Will I be able to pass back user's credentials back to source to enforce the masking and RLS set within table access policies in dataricks using a Direct Lake connection?

It seems like there are performance benefits to Direct Lake but I am unsure whether RLS and column masking is enforced like Direct Query connections. I don't want to recreate access controls within the Power BI semantic model, ideally.

Can anyone provide the best architecture for working with databricks that is both performant, secure and easy to manage? Is anyone able to provide a practical example of OneLake security and how this applies to mutiple sources and the effect in Power BI? Thanks