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Pros and cons of Azure Analysis for Tabular Data Model vs Fabric

DAX99 — Mon, 14 Sep 2026 20:14:39 GMT

I realize I'm in the Fabric Forums so understandably there will be a lot of support for Fabric, but I was just looking for some pros and cons for my situation. We are a pretty small company and currently use Azure Analysis server for our Tabular Data Model. Our business needs are such that we just have to process one of our clients at a time (generally speaking), so we have partitions set up per client. We have an S2 and S4 tier in AAS and depending on the size of the client, we'll turn on whichever of those we need to process the client partitions. However, once every few years we need to process all clients (we haven't had to do this since migrating from Microsoft SSAS to Azure Analysis Services) but we'll probably need a S8 v2 or S9 v2 to process all clients. I'm not exactly sure the size of our model when we have all clients processed, but a rough estimate would be approximately 5 GB. I'm not involved with the billing side so I don't know how much we're paying currently. But we are investigating whether it makes sense to move to Fabric for our Tabular Model for cost savings. We're not currently using Power BI for anything (although we have started to evaluate it). We only have three end users (managers) who use the data from our model. They do this with Excel Pivot tables we created to pull the data from our Azure AS database. They basically just filter the Pivot tables we set up in Excel and that gives them what they need. We're just exploring Fabric semantic models and whether this will be cost effective for us, or if it may be overkill for our use. We don't do any scheduling of processing. We're a pretty small company and basically when management needs the data to be processed, we turn on the server and kick off a SQL Agent Job that processes the Azure model. The SQL Agent job uses basic JSON to pull data from our on prem SQL Server database over to Azure AS. So having automated scheduling isn't a high priority since we're generally only processing 1 or 2 times per week. We're not using DataBricks or Pipelines other similar tools. We have pretty basic needs. Just wondering if anyone had been in a similar situation or had experience moving from Azure Tabular Model to Fabric. Thanks for any feedback!!

Approach for automating Dataflow Gen2 connection binding across Dev/Prod?

lavginqo2 — Mon, 14 Sep 2026 19:53:22 GMT

We're deploying Dataflow Gen2 from Dev to Prod via fabric-cicd with all destination Lakehouse IDs and source parameters fully parameterized. Definitions deploy cleanly.

The blocker: every query's data source connection has to be manually reconfigured in the target workspace — "Configure connection" doesn't auto-bind to an existing connection object even when one already exists for that connector, and this has to be repeated per query/table, not once per Dataflow. Docs confirm this is expected ("connections are statically bound... can't be altered using workspace variables or parameterization"), but that leaves genuine automation impossible for Dataflows with many tables (~47 in our case).

And it is even not possible to update connection. It seems that each table has to be created fresh in Prod.

Has anyone found a real workaround — Service Principal-based automation, REST API call to bind connections post-deploy etc.

What's actually working for people running CI/CD from Dev to Prod at scale here?

Thanks

Accessibility issue in the Share dialog of embedded Power BI reports

ugartema — Mon, 14 Sep 2026 19:19:21 GMT

Accessibility issue in the Share dialog of embedded Power BI reports

I would like to share an accessibility finding identified while testing an embedded Power BI report published as part of the content of an institutional website. The purpose of this post is to provide constructive feedback to the Microsoft Power BI team and the accessibility community, based on a real user experience with screen reader navigation.

Context

The issue was found during accessibility and usability testing of a Power BI report embedded in an institutional website of ARESEP, the public services regulatory authority in Costa Rica. The report was not being reviewed only as a visual dashboard, but also as interactive web content that should be usable by people who navigate with a keyboard and screen reader.

Test environment

Content tested: embedded Power BI report published within a web page.
Browsers tested on Windows: Microsoft Edge and Google Chrome.
Screen readers tested on Windows: NVDA and JAWS.
Browser tested on macOS: Safari.
Screen reader tested on macOS: VoiceOver.
Navigation method: keyboard and screen reader interaction.

Description of the issue

When navigating the embedded Power BI report, the interface includes several areas: an initial section with information, keyboard command references and an option to skip to the main content; the main Power BI content; and a footer with options such as zoom and sharing.

The accessibility issue appears when activating the Share button. After pressing this button, a modal dialog is displayed. However, keyboard and screen reader focus remains on the Share button instead of moving into the dialog. As a result, the user is not immediately informed that a dialog has opened and must manually search for the new content.

Additionally, the dialog contains a text field with the URL of the Power BI report, but there does not appear to be an explicit Copy URL button or a clear mechanism that automatically copies the URL to the clipboard. In practice, this means that a screen reader user has to locate the dialog manually, find the URL field, select or access the text, and copy it manually before being able to share the report.

Steps to reproduce

Open a web page that contains an embedded Power BI report.
Navigate through the embedded Power BI interface using the keyboard and a screen reader.
Repeat the test in Windows using Microsoft Edge and Google Chrome with NVDA and JAWS.
Repeat the test in macOS using Safari with VoiceOver.
Move to the footer area of the report where the Share button is available.
Activate the Share button.
Observe that a modal dialog appears visually, but focus remains on the Share button instead of moving into the dialog.
Try to access and copy the report URL from the dialog using only the keyboard and screen reader.

Actual behavior

The Share dialog opens, but focus is not moved to the dialog.
The screen reader focus remains on the Share button.
The user is not clearly informed that a modal dialog has opened.
The behavior was observed across the tested environments, including Windows with Edge and Chrome using NVDA and JAWS, and macOS with Safari using VoiceOver.
The URL is presented in a text field, but there is no clearly available Copy URL button or automatic copy behavior.
The user must manually locate the dialog and copy the URL, which increases effort and may create confusion.

Expected behavior

When the Share button is activated, focus should move directly into the modal dialog.
The dialog should be announced properly by the screen reader, including its purpose and accessible name.
Keyboard focus should remain within the dialog until the user closes it.
The dialog should provide a clear, keyboard-accessible Copy URL button.
Alternatively, if the focus is moved automatically to the URL field, the interface could provide a clear and accessible way to copy the URL to the clipboard.

Accessibility impact

This behavior affects people who use screen readers and keyboard navigation. If focus does not move to the modal dialog, the user may not know that new interactive content has appeared. This can make the Share feature difficult to discover, understand and use. The absence of a clear Copy URL action also increases the number of steps needed to complete a common task: sharing the report with another person.

Why this matters

Power BI reports are increasingly used as public-facing information tools. When these reports are embedded in institutional or government websites, their accessibility affects access to public information. Improving focus management and providing a clear keyboard-accessible way to copy the report URL would help ensure that users of assistive technologies can interact with Power BI content more independently and efficiently.

It is also important to note that, based on the tests performed, the main content of embedded Power BI reports can become reasonably accessible when report authors apply accessibility best practices. This includes providing clear labels, maintaining a logical tab order, using meaningful alternative text when needed, and designing interactive elements so they can be understood by assistive technologies.

However, the issue described in this post corresponds to the default embedded Power BI interface, not to the report content created by authors. Because of that, it is not something that can be fully solved through good dashboard design practices. It likely needs to be reviewed and addressed at the product interface level.

Suggestion for improvement

It would be valuable for Microsoft Power BI to review the accessibility behavior of the Share dialog in embedded reports, especially focus management when the dialog opens and the availability of a clear Copy URL button. This would improve the experience for screen reader users and align this interaction more closely with expected modal dialog behavior on the web.

I am sharing this finding as constructive feedback from accessibility testing performed in multiple browser and screen reader combinations, including Windows with Edge and Chrome using NVDA and JAWS, and macOS with Safari using VoiceOver. I hope it can help improve the embedded Power BI experience for screen reader and keyboard users.

Hallazgo de accesibilidad en el cuadro de compartir de informes Power BI incrustados

Quisiera compartir un hallazgo de accesibilidad identificado durante la revisión de un informe de Power BI incrustado como parte del contenido de un sitio web institucional. El propósito de esta publicación es aportar retroalimentación constructiva al equipo de Microsoft Power BI y a la comunidad interesada en accesibilidad digital, a partir de una experiencia real de navegación con lector de pantalla.

Contexto

El hallazgo se identificó durante pruebas de accesibilidad y usabilidad realizadas a un informe de Power BI incrustado en un sitio web institucional de la ARESEP, la Autoridad Reguladora de los Servicios Públicos de Costa Rica. La revisión no se realizó únicamente sobre el componente visual del informe, sino también sobre la interacción del contenido como parte de una página web que debería poder ser utilizada por personas que navegan con teclado y lector de pantalla.

Entorno de prueba

Contenido evaluado: informe de Power BI incrustado en una página web.
Navegadores probados en Windows: Microsoft Edge y Google Chrome.
Lectores de pantalla probados en Windows: NVDA y JAWS.
Navegador probado en macOS: Safari.
Lector de pantalla probado en macOS: VoiceOver.
Método de navegación: interacción mediante teclado y lector de pantalla.

Descripción del hallazgo

Al navegar por el informe de Power BI incrustado, la interfaz contiene varias zonas: una sección inicial con información, referencias a comandos de teclado y una opción para saltar al contenido principal; el contenido principal del informe; y un pie con opciones como zoom, compartir y otros controles.

El problema se presenta al activar el botón Compartir. Cuando se presiona este botón, se muestra un cuadro de diálogo modal. Sin embargo, el foco del teclado y del lector de pantalla permanece en el botón Compartir, en lugar de desplazarse hacia el cuadro de diálogo. Como resultado, la persona usuaria no recibe una indicación clara de que se abrió un nuevo contenido interactivo y debe buscar manualmente el modal.

Además, el cuadro de diálogo contiene un campo de texto con la URL del informe de Power BI, pero no parece existir un botón claro para copiar la URL ni un mecanismo accesible que permita copiarla de forma directa al portapapeles. En la práctica, esto obliga a la persona usuaria de lector de pantalla a localizar manualmente el modal, encontrar el campo con la dirección, seleccionar o acceder al texto y copiarlo manualmente antes de poder compartir el informe.

Pasos para reproducir

Abrir una página web que contenga un informe de Power BI incrustado.
Navegar por la interfaz del informe utilizando teclado y lector de pantalla.
Repetir la prueba en Windows con Microsoft Edge y Google Chrome, utilizando NVDA y JAWS.
Repetir la prueba en macOS con Safari, utilizando VoiceOver.
Avanzar hasta el área del pie del informe, donde se encuentra disponible el botón Compartir.
Activar el botón Compartir.
Observar que aparece visualmente un cuadro de diálogo modal, pero el foco permanece en el botón Compartir en lugar de moverse al modal.
Intentar acceder y copiar la URL del informe desde el cuadro de diálogo utilizando únicamente teclado y lector de pantalla.

Comportamiento actual

El cuadro de diálogo de Compartir se abre, pero el foco no se mueve hacia el modal.
El foco del lector de pantalla permanece ubicado en el botón Compartir.
La persona usuaria no recibe una indicación clara de que se abrió un cuadro de diálogo.
El comportamiento fue observado en los entornos probados, incluyendo Windows con Edge y Chrome usando NVDA y JAWS, y macOS con Safari usando VoiceOver.
La URL se presenta en un campo de texto, pero no hay un botón Copiar URL claramente disponible o un comportamiento automático de copiado.
La persona usuaria debe localizar manualmente el modal y copiar la URL, lo que aumenta el esfuerzo y puede generar confusión.

Comportamiento esperado

Al activar el botón Compartir, el foco debería moverse directamente al cuadro de diálogo modal.
El lector de pantalla debería anunciar adecuadamente el diálogo, incluyendo su propósito y nombre accesible.
El foco del teclado debería permanecer dentro del cuadro de diálogo hasta que la persona usuaria lo cierre.
El modal debería ofrecer un botón claro y accesible mediante teclado para copiar la URL.
Como alternativa, si el foco se mueve automáticamente al campo de texto con la URL, la interfaz debería ofrecer una forma clara y accesible de copiarla al portapapeles.

Impacto en accesibilidad

Este comportamiento afecta a personas que utilizan lectores de pantalla y navegación por teclado. Si el foco no se desplaza al cuadro de diálogo, la persona usuaria puede no saber que apareció nuevo contenido interactivo. Esto dificulta descubrir, comprender y utilizar la función de compartir. Además, la ausencia de una acción clara para copiar la URL aumenta la cantidad de pasos necesarios para completar una tarea frecuente: compartir el informe con otra persona.

Por qué es importante

Los informes de Power BI se utilizan cada vez más como herramientas de información pública. Cuando estos informes se incrustan en sitios web institucionales o gubernamentales, su accesibilidad incide directamente en el acceso a la información. Mejorar la gestión del foco y ofrecer una forma clara, accesible mediante teclado, para copiar la URL del informe ayudaría a que las personas usuarias de tecnologías de asistencia puedan interactuar con el contenido de Power BI de forma más autónoma y eficiente.

También es importante señalar que, a partir de las pruebas realizadas, el contenido principal de los informes de Power BI incrustados puede llegar a ser razonablemente accesible cuando las personas que los crean aplican buenas prácticas de accesibilidad. Esto incluye usar etiquetas claras, cuidar el orden lógico de tabulación, agregar textos alternativos cuando hacen falta y diseñar los elementos interactivos de forma que puedan ser comprendidos por tecnologías de asistencia.

Sin embargo, el hallazgo descrito en esta publicación corresponde a la interfaz predeterminada de Power BI incrustado, y no al contenido del informe creado por las personas autoras. Por esa razón, no es un aspecto que pueda solucionarse completamente mediante buenas prácticas de diseño del dashboard. Probablemente requiere ser revisado y corregido desde la propia interfaz del producto.

Sugerencia de mejora

Sería valioso que Microsoft Power BI revise el comportamiento de accesibilidad del cuadro de diálogo Compartir en informes incrustados, especialmente la gestión del foco cuando se abre el modal y la disponibilidad de un botón claro para copiar la URL. Esto mejoraría la experiencia de las personas usuarias de lectores de pantalla y acercaría esta interacción al comportamiento esperado de los cuadros de diálogo modales en la web.

Comparto este hallazgo como retroalimentación constructiva a partir de pruebas de accesibilidad realizadas en varias combinaciones de navegador y lector de pantalla, incluyendo Windows con Edge y Chrome usando NVDA y JAWS, y macOS con Safari usando VoiceOver. Espero que pueda contribuir a mejorar la experiencia de uso de Power BI incrustado para personas que navegan con lector de pantalla y teclado.

Se me recomendó crear la solicitud en ideas.fabric.microsoft.com porque inicialmente creamos el post en la sección de comunidad de Power BI. Adjunto hilo original por si se desea ver los comentarios de otros usuarios:

https://community.fabric.microsoft.com/discussions/power-bi-designer/accessibility-issue-in-the-share-dialog-of-embedded-power-bi-reports/5366350

Restaurant & Menu Engineering — Analysis

FarhanJeelani — Mon, 14 Sep 2026 18:47:26 GMT

Most dashboards answer questions. Very few tell you what to do on Monday.

I rebuilt a restaurant analytics report this week and spent as much time on the narrative as the numbers.

What that meant in practice:

Every page ends with a written read-out panel. Not a KPI. An actual sentence explaining what the chart means and what it implies. "Labour averages 23.2% of sales, but The Bistro Table runs 60.4%, and it is a scheduling problem, not a menu problem."

Colour carries meaning, not decoration. Stars are teal, Plow Horses blue, Puzzles amber, Dogs coral, and those four colours mean the same thing on every page.

The menu engineering matrix uses reference lines at the actual portfolio benchmarks, so the quadrants are real thresholds rather than eyeballed corners.

The icon set is hand-drawn SVG rather than stock clipart, rendered at 4x so it stays crisp on any display. Shipped in both a dark and a light theme.

And a final page that does nothing but list the three model issues I found and the corrected DAX to fix them. If the numbers are wrong, the design is decoration.

Six pages. One dataset. 17 venues, 167 menu items.

Happy to share the DAX fix pack if it is useful to anyone.

Issue with table visual on Power BI Desktop

hahayes — Mon, 14 Sep 2026 18:18:16 GMT

I am having an issue with Power BI Desktop table visualization. This problem started last week and i did not face this issue during August 2026. The visual shows how clean the data looks which is from August 2026 on the left but the one I have is from September 2026 and the table visual looks super weird which is on the right. I want to fix my table visual so it looks like the one on left! Most of my visuals have been acting stramge as well

Really need help as I need to create a report which is due by wednesday! thank you in advance

Eventhouse Event Hub Ingestion: Respect retrieval start date during preview sampling

dskiftesvik — Mon, 14 Sep 2026 17:39:34 GMT

In the Eventhouse / KQL Database "Get Data" (One-Click) ingestion wizard for Azure Event Hubs, the preview/inspection engine samples messages starting from the oldest retained events in the partition (offset 0), completely ignoring the configured Event retrieval start date.

If a producer transitions formats (e.g., switching payload compression from plain JSON to GZip, or updating a schema) on an active Event Hub, the wizard fails to decompress or parse the older historical records at offset 0.

As a result, validation fails and the "Finish" button remains disabled, blocking connection creation—even when all messages from the selected start timestamp onwards are completely valid.

Requested Solution:

Respect Start Date during Preview: Ensure the sample reader starts querying from the user-configured "Event retrieval start date" rather than partition offset 0.

Option to Skip Preview / Sampling: Provide a checkbox or advanced mode to bypass data sampling altogether and allow selecting an existing table and pre-defined ingestion mapping (aligning with ARM/Bicep template deployments and standard Kusto data connections).

function logical error

Dicken — Mon, 14 Sep 2026 17:27:46 GMT

Hi, i kinda get why this error happens but thougt someone might have thoughts insights into the
error problem;

i i start with a true false ;

= let test =( x)=> Number.Round(x) = x
in test

but if i then us ;

if test then "whole" else "decimal"

i get an eroor but i can use ;

= (x)=> if Number.Round(x) = x then "whole" else "decimal"

sorry for the spacing , I could not get code tags to work and the formatting kept goiong all over the plaice.

why did i have to go thru the rest connection's properties again on my pipeline's copy job

db042190 — Mon, 14 Sep 2026 17:11:16 GMT

hi i was in a rush when this happened and can post more detail here if necessary, but last week when i went to run my pipeline which was already working, it seemed to me my rest connection to an sap api was inactivated and fabric was basically saying it needed to either be inactivated or fixed. That was unexpected and it seemed to me i was forced into more or less revisiting the copy job's setup steps in order for the problem to go away. i didnt need to reset the conn's credentials but i did need (it seemed) to "sign in" again to pbi. Can somebody tell me what this is all about? How is this going to run unattended if this kind of thing happens again?

Grounding Fabric Data Agents so they stop inventing columns | Part-1

FarhanJeelani — Mon, 14 Sep 2026 17:04:12 GMT

Your data agent answers a question about churn by writing SELECT customer_id, churn_flag FROM dim_customer. There is no churn_flag column. There never was. The agent produced confident SQL against a column it made up, the query failed, and the person who asked has now decided the whole thing is a toy.

This is the single most common complaint I see about Fabric data agents in the community, and almost every answer to it is the same: "add better AI instructions." That advice is not wrong, but it is the weakest lever available and people reach for it first.

Here is the thing that changes how you approach the problem. An agent does not invent a column because it is disobedient. It invents a column because the question implied a concept that your schema does not name, and generating something plausible is the only move it has. You cannot instruct your way out of that. You have to close the gap between the words your users use and the words your schema uses.

So the fix is a stack, and it is worth being blunt about which layers actually carry weight:

Layer	Effort	How much it actually helps
Shape the schema the agent sees	High	Most of the win
Scope tables and columns tightly	Low	Large
Data source instructions and descriptions	Medium	Moderate
Example queries (few-shot)	Medium	Moderate, and underused
Agent instructions	Low	Small on its own
Evaluation harness	Medium	Not accuracy, but it is how you keep accuracy

Most teams do this list backwards. Below is each layer with the actual configuration.

First, confirm it is actually hallucinating

Before you fix anything, find out what happened. Two different failures look identical from the chat window:

The agent referenced a column that does not exist.
The agent referenced a real column that means something different from what the user assumed.

The second is far more common and far more dangerous, because it returns a number instead of an error. A status column that means shipment status answering a question about payment status will produce a clean, wrong answer that nobody catches for a quarter.

Use the Diagnostics button in the data agent. <cite index="47-1">It downloads a snapshot of the agent's configuration and execution steps, including data source settings, the instructions that were applied, which example queries were used, and the steps the agent took to produce its response.</cite> Read the generated SQL, not the answer. If you are debugging by reading answers, you are debugging the wrong artifact.

Layer 1: Shape what the agent can see

The agent reads your schema. If your schema is tbl_cust_mstr with columns flg1, dt_2, and amt_ttl, no instruction block will save you.

Build a view layer for the agent. Do not point it at your raw tables. Create a schema of AI-facing views with names that match how people actually speak, and expose only those.

-- Fabric Warehouse (T-SQL). The agent sees this, not the underlying tables. CREATE SCHEMA ai; GO CREATE VIEW ai.customer AS SELECT c.cust_sk AS customer_key, c.cust_no AS customer_number, c.cust_nm AS customer_name, g.ctry_cd AS country_code, g.ctry_nm AS country_name, c.seg_cd AS segment_code, CASE c.seg_cd WHEN 'E' THEN 'Enterprise' WHEN 'M' THEN 'Mid-Market' WHEN 'S' THEN 'Small Business' END AS segment_name, c.acq_dt AS acquisition_date, CASE WHEN c.term_dt IS NOT NULL THEN 1 ELSE 0 END AS is_churned, c.term_dt AS churn_date, c.act_flg AS is_active FROM dbo.dim_cust c LEFT JOIN dbo.dim_geo g ON g.geo_sk = c.geo_sk WHERE c.is_current = 1; -- collapse SCD2 so the agent never has to reason about it GO

Four things happened there, and each one removes an entire class of hallucination:

is_churned now exists. The agent no longer has to invent it. This is the whole game. Every time you see a hallucinated column, ask whether it should be a real column.
Codes are decoded. segment_name means the agent never has to guess that 'E' is Enterprise, and users can ask about "enterprise customers" in their own words.
SCD2 is collapsed. Slowly changing dimensions are a reliable source of silently wrong answers, because the agent has no reason to know it must filter to the current row.
Cryptic names are gone. amt_ttl invites guessing; total_amount_usd does not.

If you cannot create views, at minimum use column descriptions on the tables you do expose. The SDK <cite index="41-1">supports adding column and table descriptions for SQL data sources</cite>, and they carry real weight in query generation.

Then scope hard. <cite index="46-1">A data agent supports up to five data sources, and for each one you select the specific tables the agent can use.</cite> Select fewer than you think. Twelve well-described tables outperform sixty raw ones, every time. Ambiguity is the raw material of hallucination, and every table you add manufactures more of it.

One trap specific to lakehouses: <cite index="46-1">the agent answers using the lakehouse tables you select and does not read standalone files such as CSV or JSON unless they are ingested or exposed as tables.</cite> If your answer set lives in Files, the agent cannot see it, and it will improvise around the gap rather than tell you.

Layer 2: Data source instructions (per source, not global)

There are two distinct instruction fields and people conflate them constantly.

Data source instructions attach to a single data source and are used during query generation. <cite index="31-1">They improve the agent's ability to select the right tables and columns, understand data-specific logic such as fiscal calendars and regional codes, apply consistent filters, and interpret intent.</cite> This is where anti-hallucination content belongs, because it sits closest to the SQL being written.

Here is a template. Replace the specifics; keep the structure.

SCOPE This source contains the ai.* views only. Every question about customers, subscriptions, invoices and revenue is answered from these views. TABLE MAP ai.customer – one row per current customer. Grain: customer_key. ai.subscription – one row per subscription. Grain: subscription_key. A customer may hold several subscriptions. ai.invoice_line – one row per invoice line. Grain: invoice_line_key. This is the only source of revenue. ai.date – calendar and fiscal date dimension. Grain: date_key. JOIN PATHS ai.subscription -> ai.customer on customer_key ai.invoice_line -> ai.subscription on subscription_key Any table -> ai.date on date_key Never join ai.invoice_line directly to ai.customer. Go through ai.subscription, or revenue will be duplicated across subscriptions. DEFINITIONS "Revenue" = SUM(ai.invoice_line.net_amount_usd). Never gross_amount_usd. "Churn" = ai.customer.is_churned = 1. "Active customer"= ai.customer.is_active = 1. "ARR" = SUM(ai.subscription.annual_contract_value_usd) WHERE ai.subscription.status = 'Active'. "Enterprise" = ai.customer.segment_name = 'Enterprise'. TIME The fiscal year starts on 1 April. For any question mentioning a fiscal period, use ai.date.fiscal_year and ai.date.fiscal_quarter. For any question mentioning a calendar period or naming a year with no other qualifier, use ai.date.calendar_year. "Last month" means the most recent complete calendar month, not the trailing 30 days. VALUE FORMATS country_code is a two-letter ISO code ('AE', 'SA', 'GB'), never a full country name. To filter on a country name, use country_name. segment_name is one of: Enterprise, Mid-Market, Small Business. All amounts are already in USD. Do not apply currency conversion. CONSTRAINTS Use only the columns listed in the table map above. If a question requires a column or a concept that does not exist in these views, do not substitute a similar column and do not construct one. State which specific column would be needed and stop.

That last paragraph is the only genuinely instruction-shaped anti-hallucination content in the whole block, and it is the least important part of it. Everything above it works by removing the need to guess. The constraint paragraph only catches what slipped through.

Note the join warning. Fan-out across a one-to-many join is the most common cause of a plausible-but-doubled number, and it produces no error at all.

Layer 3: Agent instructions (routing and terminology)

Agent instructions are global. <cite index="28-1">You can write up to 15,000 characters of plain English to instruct the agent on how to handle queries, including which data source to use for which kind of question, and definitions for words, acronyms or terms the AI consistently misinterprets.</cite> <cite index="46-1">For example: direct financial metrics to a Power BI semantic model, raw data exploration to the lakehouse, and log analysis to the KQL database.</cite>

Keep this layer short and about routing. Detail belongs at the source level.

ROUTING Questions about revenue, ARR, bookings or any officially reported financial metric -> the "Finance Reporting" semantic model. These figures are governed and must match published reporting. Questions about customer counts, subscription details, product usage or any exploratory or ad-hoc analysis -> the "CustomerLH" lakehouse. Questions about system errors, latency, ingestion failures or anything described as logs, events or telemetry -> the "PlatformEvents" KQL database. If a question spans finance and customer detail, answer the financial part from the semantic model and say explicitly which part came from which source. ORGANISATIONAL TERMS "GCC" = the countries AE, SA, KW, QA, BH, OM. "MENA" = GCC plus EG, JO, LB, MA, TN. "NRR" = net revenue retention. Only reported from the Finance Reporting model; do not compute it from raw invoice data. "Logo" = a customer account, not a brand asset. "Logo churn" means customer count churn, not revenue churn. BEHAVIOUR Always state which data source answered the question. When a question is ambiguous between two definitions, ask one clarifying question rather than picking one silently. Never present an estimate as a reported figure.

The terminology section earns its place. "Logo churn" is exactly the kind of phrase that sends an agent hunting for a column that does not exist.

One boundary worth understanding: <cite index="46-1">instructions sit in a precedence model beneath organisational policy and role-based permissions, and above end-user prompts. Where they conflict with policy, the agent refuses or redirects.</cite> Your instructions configure behaviour; they do not grant access.

Layer 4: Example queries, which almost nobody uses properly

This is the most underused surface in the whole product. <cite index="46-1">You can add sample question-and-query pairs that show the agent how to interpret similar questions</cite>, and <cite index="46-1">you get up to 100 per data source.</cite> Most agents I look at have three.

Few-shot examples do something instructions cannot: they demonstrate the join path, the filter convention, and the column vocabulary in the exact form the agent has to produce. An instruction saying "always join through subscription" is a claim. An example query showing it is a pattern.

Pick your examples deliberately. Cover:

The canonical join, so the fan-out path is demonstrated rather than described.
The fiscal-versus-calendar distinction, one example each.
The metric everyone asks for differently. If revenue is asked as "sales", "turnover" and "top line", write one example per phrasing.
The concept your schema does not have. More on this below.

-- Q: What was our revenue in the GCC last fiscal quarter? SELECT SUM(il.net_amount_usd) AS revenue_usd FROM ai.invoice_line il JOIN ai.subscription s ON s.subscription_key = il.subscription_key JOIN ai.customer c ON c.customer_key = s.customer_key JOIN ai.date d ON d.date_key = il.date_key WHERE c.country_code IN ('AE','SA','KW','QA','BH','OM') AND d.fiscal_quarter = ( SELECT fiscal_quarter FROM ai.date WHERE date_key = CAST(GETDATE() AS DATE) ) - 1;

-- Q: How many enterprise customers churned in calendar 2025? SELECT COUNT(DISTINCT c.customer_key) AS churned_customers FROM ai.customer c JOIN ai.date d ON d.date_key = c.churn_date WHERE c.is_churned = 1 AND c.segment_name = 'Enterprise' AND d.calendar_year = 2025;

One limitation to plan around: <cite index="46-1">example query pairs are not currently supported for Power BI semantic model data sources.</cite> If you route financial questions to a semantic model, that source gets instructions and a well-described model, and nothing else. Invest correspondingly more in the model's own metadata and measure descriptions.

There is also a preview assistant that can help you generate this configuration. <cite index="27-1">Build agent with AI mode summarises key entities, likely join paths and important columns, surfaces patterns from successfully executed queries in your query history, and proposes candidate few-shot examples and data source instructions.</cite> <cite index="27-1">It is currently limited to SQL and Eventhouse sources and will not run if you add an unsupported source.</cite> Treat its output as a first draft you edit, not a configuration you accept.

Please check part-2 of this post

Diagnose Fabric Data Warehouse workloads with the SQL DW operations skill (Generally Available)

Mariyaali — Mon, 14 Sep 2026 17:00:00 GMT

When a warehouse slows down, the investigation usually starts with more questions than answers. Was there a capacity spike? Did a specific query suddenly become expensive? Are requests failing, being canceled, or simply taking longer than usual?

Answering those questions often requires switching between the Fabric Capacity Metrics app, Query Insights, and SQL pool diagnostics while manually correlating time ranges across multiple tools. The SQL DW operations skill brings those investigations into a single workflow and is now generally available.

Available through the open-source Microsoft Fabric skills repository, the SQL DW operations skill lets you describe a problem in natural language using a compatible AI coding tool such as GitHub Copilot CLI. The skill runs bounded, read-only diagnostics and returns a structured diagnosis, supporting evidence, recommended actions, and validation steps.

What you can do with the SQL DW operations skill

The SQL DW operations skill helps you:

failure-analysis: Separate failed queries from canceled requests, identify affected workloads, and resolve engine error codes.
resource-consumers: Find recurring resource-consuming query patterns, regressions, and changes in execution volume or per-run cost.
capacity-metrics-correlation: Connect a Capacity Metrics spike to warehouse activity in the same time window.
pool-pressure: Diagnose contention and identify workloads that might benefit from custom SQL pools.
lakehouse-health: Find lakehouse tables with small-file, deleted-row, or checkpoint issues.
query-reference: Use the appropriate read-only system views and Query Insights queries for bounded operational analysis.
scenarios: Combine the diagnostics into guided workflows for common warehouse incidents.

Each response separates the diagnosis, evidence, ruled-out causes, recommendations, and customer follow-ups. Measurements are tied to their source, and zero-row results are treated as valid evidence instead of prompting an invented explanation.

Common use cases

Start with the operational question rather than selecting system views or writing diagnostic SQL.

Figure: GIF depiction of how the SQL DW operations skill connects a natural-language prompt to bounded, read-only diagnostics and customer follow-up actions.

Investigate failed and canceled queries

Analyze failed and canceled queries in SalesWarehouse during the last 24 hours.

The skill uses Query Insights to identify affected users, applications, query patterns, and SQL pools. It resolves failed engine codes through sys.messages and keeps cancellations separate because they can reflect a user-initiated cancellation, a client timeout, or resource pressure.

Explain a performance slowdown

Explain why FinanceWarehouse was slow between 09:00 and 11:00 UTC yesterday.

The skill checks SQL pool pressure, overlapping requests, CPU, elapsed time, and storage scans. It distinguishes contention from a directly expensive query or a broad increase in workload.

Find resource-consuming query patterns

Find the top resource-consuming queries in SalesWarehouse and compare them with the previous seven days.

The skill groups requests by query shape and separates higher execution volume from increased per-run cost, new query patterns, and one-time expensive runs.

Investigate a capacity spike

Use the Fabric Capacity Metrics app to investigate the CU spike from 14:00 to 15:00 UTC, then identify expensive SQL users and query patterns.

Following the warehouse metering update introduced in August 2026, Capacity Metrics shows when consumption occurred and how much was reported based on allocated warehouse compute over time. Query Insights explains what ran during the same period.

The skill discovers the installed Capacity Metrics model, identifies a costly warehouse or SQL analytics endpoint, and analyzes Query Insights requests that overlap its time window. It doesn't join Capacity Metrics operation identifiers to Query Insights statement identifiers. Capacity consumption and warehouse CPU are complementary signals, not interchangeable measurements.

Assess custom SQL pool candidates

Assess whether recurring workloads in SalesWarehouse are candidates for custom SQL pools based on the last 30 days.

If repeated pressure is associated with a consistent application name, such as an ingestion service or reporting application, the skill can recommend testing that workload in a custom SQL pool. It identifies the application to isolate and the pressure, latency, CPU, scan, and failure measures to compare before and after the pilot.

Get started

Prerequisites

Before you start, make sure you have:

GitHub Copilot CLI or another compatible AI coding tool.
An active Fabric warehouse or lakehouse SQL analytics endpoint.
Contributor or higher access to the workspace.
The Microsoft Fabric Capacity Metrics app installed for capacity-spike investigations.

Add the Microsoft Fabric skills marketplace in GitHub Copilot CLI:

/plugin marketplace add microsoft/skills-for-fabric

Install the Fabric skills bundle:

/plugin install fabric-skills@fabric-collection

Then open Copilot CLI in a project folder and describe the warehouse issue you want to investigate. Include the workspace, warehouse or SQL analytics endpoint, and UTC time range when possible.

For detailed permissions, setup, supported scenarios, and diagnostic time limits, see Diagnose warehouse workloads with the SQL DW operations skill.

Next Steps

Install or update the Microsoft Fabric skills bundle.
Review Query Insights in Fabric Data Warehouse.
Review monitoring options for Fabric Data Warehouse.
Learn about custom SQL pools.

From Business Events, Fabric Events, and Azure Events to Real-Time Hub

robece-msft — Mon, 14 Sep 2026 16:59:28 GMT

Welcome to the eighth and final post in our Business Events, Fabric Events, and Azure Events series for Microsoft Fabric. This series takes you from foundational event-driven concepts to practical implementation patterns that help teams turn meaningful business moments into trusted signals, decisions, and actions across Fabric.

Exrtract all workspace lineage list in service

Prasanth460 — Mon, 14 Sep 2026 15:41:45 GMT

Hi Experts,

I need a help on how to extract the lineage of all workspaces, reports inside those workspaces and data models connected to that reports and data base that semantic model used to connect.

layers: Workspace--> report--> semantic model--> data source(eg: AAS model).

I can get list till 3 layers but unable to get details of 4th layer(data source) using admin monitoring folder data.

I have fabric and platform admin access. could you please help me to know if there is a way to extract complete details in power bi service. Unfortunatly, I don't have access to run API's directly,

your support helps to get the detailed list of all the workspaces in power BI service.

view 'my posts'

Dicken — Mon, 14 Sep 2026 15:07:03 GMT

Hello,
I used to be able to go to 'account' and select view subscriptions and it would bring up my
previous questions, this seems to have been removed. so how do i see my posts?

Power BI Theme Gallery Redirecting to an Incorrect URL

ajaythomas — Mon, 14 Sep 2026 14:00:59 GMT

Power BI Theme Gallery are being redirected to an incorrect URL after new update

https://community.fabric.microsoft.comhttps//community.fabric.microsoft.com/category/powerbi/discussions/themesgallery

This issue impacts the user experience and prevents successful navigation to the expected destination.

Forecasting with Autoregression, ARIMA & AIC / BIC for Data Science in Microsoft Fabric

Sahir_Maharaj — Mon, 14 Sep 2026 13:57:38 GMT

In this edition, we’re exploring forecasting through Autoregression, ARIMA, and the model selection tools AIC and BIC. By the time you’re done reading, you’ll understand how data can actually learn from its own patterns, how ARIMA helps bring structure to unpredictable trends, and how AIC and BIC keep your models grounded by balancing accuracy with simplicity.

How to Connect SAP to Power BI Through SAP BTP

metrica — Mon, 14 Sep 2026 13:57:05 GMT

Learn how to connect SAP to Power BI through SAP BTP using Metrica Software’s connector. This step-by-step guide covers creating reusable OData data sources, managing access, and loading SAP data into Power BI.

Schema Compare in VS Code: Simplifying Fabric Warehouse Deployments

techies — Mon, 14 Sep 2026 13:55:53 GMT

Deploying databases shouldn't feel uncertain. Synchronizing development and production schemas in Microsoft Fabric Warehouse can be challenging as database objects evolve. A frequent issue arises when the .sqlproj file isn't configured with the Fabric-specific schema provider (SqlDbFabricDatabaseSchemaProvider). Misconfiguration may lead Schema Compare to flag supported Fabric objects as unsupported. To avoid this issue, explicitly specify the schema provider in the .sqlproj XML if the GUI does not offer that option.

In this blog, let's take a quick look at how to get started.

Before using Schema Compare with Fabric Warehouse, make sure you have the following prerequisites.

You need access to an existing Warehouse item within a Microsoft Fabric workspace, with Contributor or higher permissions.
You also need Visual Studio Code installed on your workstation.
Next, install the .NET SDK, which is required to build and publish database projects.
Finally, install these two Visual Studio Code extensions:

SQL Database Projects
SQL Server (mssql)

Both extensions are available directly from the Visual Studio Code Marketplace.

After installing the extensions, open Visual Studio Code and select "Add Connection" to connect to Fabric Warehouse.

Enter the required server and authentication details, and complete the connection.

After connecting, access the Warehouse directly in Visual Studio Code.

Open Schema Compare

Now navigate to Database Projects in Visual Studio Code.

You should see your available database projects and connections. Right-click the database project or connection and select "Schema Compare."

Schema Compare gives you an object-level view of the differences between the source and target.

You can compare schemas between:

.dacpac files
Databases
SQL database projects

Comparison results outline actions to align target with source.

Instead of treating the database as a single deployment unit, you can review individual changes and decide what should happen next.

You can also selectively exclude actions from the comparison results when a particular change should not be deployed.

Schema Compare with Fabric Warehouse

Schema Compare's integration with Fabric Warehouse allows developers to identify differences in database objects before implementing changes.

Database Projects and Git

The database project becomes the artifact under review in the PR, not a raw DDL diff — schema changes get the same scrutiny as application code, and the deployment pipeline consumes a validated project state rather than an ad-hoc script.

Schema Compare exposes Fabric Warehouse DDL limitations before deployment, helping you proactively address issues and maintain greater control over the deployment process.

Power BI Smart Table Visual: Excel style Column Filtering, Dynamic Column Headers, Grouping Columns

tharunkumarRTK — Mon, 14 Sep 2026 13:55:10 GMT

For a smoother reading experience with clearer screenshots and easy code copying, I suggest [reading this article on my website](https://www.techietips.co.in/articles/powerbi-managed-6500measures-smartly). It’s free, ad-free, distraction-free, and dedicated solely to sharing knowledge.

The Table is the most commonly used visual in Power BI. Anyone who has spent years in Excel expects to click a header and filter that column. They expect related columns to sit under a common heading. The native table visual does none of this.

There are grid visuals in the Microsoft visual app source that do. Most developers never get to use them, for two reasons that have nothing to do with the features. The good ones are licensed per developers or user, and that cost has to be justified to someone. The free ones are usually not Microsoft certified, which means an uncertified third party script is running inside your report, and that is a fair thing for a security team to say no to.

So I built one. It is called Smart Table, and I built it with Claude. This post walks through what it does.

1. Excel style column filtering

Every column header carries a funnel button in its own divided cell. Click it and you get what you would expect from Excel: sort, a set of conditions appropriate to the column’s type, and a searchable checkbox list of that column’s values. The menu names the column it belongs to, counts what you have ticked, and stays open while you sort.

The conditions follow the column type rather than offering one generic list:

Sync: does the filter stay in the grid or reach the report

Sync is on by default. Filters applied in the header are pushed to the model, so every other visual on the page responds, exactly as if the user had used a slicer. Turn Sync off: Filtering then narrows this grid only.

Global search

The toolbar has a search box with its own operator dropdown: contains, is exactly, starts with, ends with, does not contain. It searches across every column at once.

Power BI: Smart Table Excel Style Column Filtering

2. Dynamic column headers, driven by a measure

A column header name is normally a static label. Here it can be a DAX measure.

Arrival Month is set to April, and the Key Metrics headers read Lead Time (Avg: 30.00), Room Nights (Avg: 1.00), ADR (Avg: 160.50). Change the month and they recompute.

The header honors the filter context, including the filters applied inside the grid itself. Filter Property down to Resort Hotel with the funnel and the averages in the header follow.

Power BI Smart Table: Measure Driven Column Headers (Dynamic Column names) - YouTube

3. Column groups, without any extra tables

Four groups across eleven columns: Reservation, Channel, Room Type, Key Metrics. Each one is a spanning header above its columns with its own colour.

The thing to notice is what is not involved. No disconnected table. No field parameters. No two table visuals stacked on top of each other. No shapes placed in the background: which means your column groups move to the right along with your cursor.

Power BI Smart Table: Grouping columns in a table without using a static table and a matrix visual - YouTube

Known limits

All of these come down to one idea: some filtering reaches the model and some is local to the grid.

30,000 rows That is the Power BI cap for a table mapping with this data reduction algorithm. Header filters push a real model filter, so filtering down to a workable set works fine. Do not point it at an unfiltered fact table.
Value lists cap at 2,000 distinct values per column**, built from the loaded rows. On a high cardinality column like a guest name, use the search box in the menu.
Ends with, Does not end with, and blank selections filter the grid only.** Power BI’s advanced filter operators have `StartsWith` and `Contains` but no `EndsWith`, and a blank has no equivalent in a model side `In` list. The menu tells you when you pick one of these.
Bookmarks restore the data but not the ticked checkboxes.** The model filter is persisted by Power BI, so the rows come back correctly. The header checkboxes just will not show as ticked.

I wrote this to make one point: the gaps in the built-in visuals are not permanent. Build your own, and with vibe coding that is no longer a months-long project.

I am not sharing the .pbiviz file. It is an experiment, not tested for production grade reports. Feel free to checkout my [website](https://www.techietips.co.in) and reach out to me if you are interested to do these experiments along with me,

I would like to continue developing this visual and will write detailed blogs in features explaining its features and implementations.

I hope you learned something new. Feel free to share your thoughts in the comments section.

Happy Learning!!!

How to add RLS on top of FUAM datasets

TobNyg — Mon, 14 Sep 2026 13:42:15 GMT

We are testing the FUAM reports and would like to share some of them with workspace administrators.

To achieve this, we want to implement RLS so that workspace owners can only see data for the workspaces they own.

I tried creating a composite model with an RLS security table containing User and WorkspaceName, and created a relationship to the Workspaces table.

However, the filtering does not seem to work properly for the Workspaces table, which is running in DirectQuery mode.

Has anyone implemented RLS for FUAM before, and if so, what approach would you recommend?

AdminCartograph: a Power BI custom visual for multi-layer subnational maps.

Nabilmj — Mon, 14 Sep 2026 13:40:05 GMT

Repo: AdminCartograph on github

Sideload the shipped .pbiviz from releases on AdminCartograph on github

The problem

Power BI's stock map visuals don't handle subnational boundaries the way governments, humanitarian orgs, and analysts working with OCHA / P-code data actually need. Shape Map has no labels, no drill, no bubbles-on-boundaries. Filled Map is Bing-tile-bound and doesn't take custom TopoJSON. Everyone I've talked to has the same workaround: export to QGIS, screenshot, paste into PowerPoint. AdminCartograph replaces that whole loop with one visual.

What it does

Bring your own geometry. Custom upload takes any GeoJSON or TopoJSON at design time (Admin1 required, Admin2 optional). Field-mapping dropdowns auto-detect ADM1_PCODE / pcode_1 / gid_1 / state_id and let you override. The uploaded geometry persists inside the .pbix — no distributing side-files. Eight OCHA countries ship in the bundle for quick starts (AFG, COD, HTI, IRN, LBN, SDN, SYR, YEM); everyone else uses the upload flow.

Five stacked layers. Choropleth (quantile / equal / manual / categorical), bubbles (√-area scaled, constant on-screen size at zoom), pie / donut / column / concentric glyphs per polygon, a full label engine, country outer glow.

Drill. Click an Admin1 → zoom to its Admin2 children, neighbour states dim to 55 %, three viewport-aware top-bar layouts, prev / next arrows that respect any external slicer's filter set.

Cross-filter both ways. Click a polygon → slicers narrow. Slicers narrow → the map auto-zooms to the filtered subset (Admin1 and Admin2 modes both).

Labels done properly. Area-weighted centroid with polylabel fallback for concave shapes, halos via paint-order: stroke fill, per-card Name source and Value source dropdowns, hide-on-overflow, constant on-screen size on zoom.

Legends: five of them. Choropleth, bubble, glyph categories, Admin-levels stroke samples, and a Values legend explaining what each label number means. Combined into one styled container when they share a corner.

Export. PDF / PowerPoint / page-image via the host's renderingFinished signal — the map renders inside the exported deck instead of showing "visual not supported". Plus an SVG-download modal for print-quality vector output.

Real-world touches. Admin1 / Admin2 aliases (Governorate / District, State / Locality), PCODE-tolerant matching (SD-01 == SD_01 == SD01), a name-based link field so you can join by state name when your data has no P-codes, an on-canvas mismatch banner that shows sample keys from both sides when nothing joins.

Under the hood

~7 K lines of TypeScript on top of powerbi-visuals-api 5.11, d3-geo, topojson-client. Full offline; embedded geometry via require() at bundle time. Format-pane model is strongly typed through formattingSettings (17 cards, deep-linked from the wiki). MIT-adjacent ISC license; the bundled COD boundaries carry OCHA / fieldmaps.io terms.

Docs

Every card, every setting, every failure mode is documented in the wiki: AdminCartograph wiki on github. The Install and Use page is the tour, Custom Geometry Upload is the killer feature's walkthrough.

Feedback and issues welcome. Especially interested in hearing which non-bundled countries people would use it with — the upload flow works for anywhere, but real-world reports of it in production are the best signal.