rss.livelink.threads-in-node

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

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

Hi everyone,

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

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

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

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

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

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

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

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

What we did (in about 60 working days):

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

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

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

OData Connection Fails with Credentials Required Error in Power BI Service

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

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

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

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

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

Hi My name is Haris,

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

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

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

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

Thanks in advance

Optimising Fabric Capacity Usage

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

Hi all,

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

Approach 1: Materialising Views as Delta Tables

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

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

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

My questions are:

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

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

Approach 2: Shifting Transformation Logic to the Semantic Model

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

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

My questions here are:

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

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

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

Thanks in advance!

Sales forecast based on historical data

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

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

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

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

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

Overrule Calendar tabel connection

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

Hi community

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

But the trick is to overrule the date.

Date end date start date payment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

VAR _Day = Max [tabel[Date])

RETURN

CALCULATE(

SUM(Tabel[Payment]),

Fact_Privatsikring[betaling_start_dato] <= _Day &&

Fact_Privatsikring[betaling_slut_dato] >= _Day)

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

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

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

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

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

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

Allows blank
Does not allow null.

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

Offline Meetup in october

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

Hi All,

We are planning an pune offline meetup in october month.

Anyone interested in organizing it can dm me separately

Regards,

Praful

Fabric Metadata & Data Quality Assessment Framework

thisismukund — Fri, 18 Sep 2026 02:31:34 GMT

Overview

A reusable Microsoft Fabric notebook for metadata discovery, single-pass PySpark data profiling, rule-based data quality assessment, and Data Quality Index (DQI) scoring.

Key Capabilities

Dual Execution Modes: Run on synthetic e-commerce data or target existing Delta tables in OneLake.

High-Performance Profiling: Single-pass PySpark aggregations compute distinct counts, null ratios, and boundaries in a single Spark action call.

Multi-Dimension Quality Auditing: Evaluates Completeness, Uniqueness, and Validity using custom weighted thresholds.

Executive Reporting: Generates an aggregate Data Quality Index (DQI) score and automated remediation guidance.

Use Cases

Lakehouse data quality monitoring and health checks
Data product readiness for Gold layer consumption
Pre- and post-migration validation
Enterprise data platform modernization

Full Notebook & Code: Download the complete .ipynb notebook file from the Github Repository

Hospital Ops Dashboard

SujataNarayana — Fri, 18 Sep 2026 01:54:07 GMT

Building operational dashboards with recommended actions and custom UI is easier than ever with Fabric apps!

In this example Fabric app, you see a nurse staffing dashboard to help hospital admins to manage understand where they are understaffed and recommended actions.

In the detailed view you see custom UI to help manage coverage across shifts and custom tooltip with action to request overtime shift.

ways of converting snowflake to star

powerbiexpert22 — Fri, 18 Sep 2026 01:29:21 GMT

are there any other ways of converting snowflake schema to star schema apart from merging dimensions?

Incremental Refresh Decision

powerbiexpert22 — Fri, 18 Sep 2026 01:20:44 GMT

I am creating fresh (new) semantic model, I am not sure whether I should implement incremental refresh now or later point of time . I do not want to change or overwrite semantic model once it move to production envionment since it may impact existing reports. ?

Check earlier questions or history

powerbiexpert22 — Fri, 18 Sep 2026 00:54:29 GMT

How can I check my earlier questions in this new theme forum?

Matcha Ops Dashboard

SujataNarayana — Fri, 18 Sep 2026 01:44:48 GMT

With Fabric apps it's easier than ever to build custom dashboards that can both analyze data and writeback to the source!

Here in this example Fabric app, you see a fully custom dashboard analyzing the matcha sales with a breakdown of the top selling drinks:

Within the same Fabric app, you can see the latest inventory and mark which drinks are sold out without having to go to a separate tool:

Veterans Affairs (Standardized Design.VA.gov) | Colorblind-Accessible

KCoderVA — Thu, 17 Sep 2026 22:20:21 GMT

A Power BI theme customized for red-green (Deuteranopia/Protanopia) color deficiencies, but still aligning with the current U.S. Department of Veterans Affairs Design System (VADS) & U.S. Web Design System (USWDS) design standards. Color values primarily use the VADS semantic tokens, typography, palettes, and styles to allow reports to closely match modern VA.gov digital styling standards, but with key changes to a palette that stays distinguishable under red-green (Deuteranopia/Protanopia) color vision.

What's included: a Deuteranopia/Protanopia-accessible containing data color sequence reordered so no two adjacent series form a red-green pair and series separate by both hue and lightness; sentiment mapped to blue (positive) and orange/gold (negative), instead of green/red for conditional formatting, among other comprehensive per-visual defaults across the accessible theme visuals, cards, tables, matrices, slicers, KPIs, gauges, charts (bar, column, line, area, scatter, waterfall, pie/donut, treemap, funnel, ribbon, etc.), reference/trend/error lines, tooltips, and the filter pane. Text, titles, axes, gridlines, and legends are pre-styled defaults for a clean, consistent look, with white canvas backgrounds and a very subtle tint on visuals, while font sizing favors readability. Designed for VA analysts, programmers, designers, or employees who prefer publishing materials with Deuteranopia/Protanopia color vision deficient target audiences in mind, while still using standardized VADS/USWDS-consistent Power BI reports features and defaults. Color values alone should not be the only way data is encoded, pair this standardized accessible theme labels, markers, or patterns for full accessibility.

Notes: Licensed under Apache-2.0. & based on the VADS color palette, as published on https://design.va.gov. The standard Veterans Affairs color theme (without color-deficient accessibility features) is also available separately in the Microsoft Fabric theme gallery.

Healthcare Claims: Finding Rework and Hidden Process Paths with Threadseer

JasonWittenauer — Thu, 17 Sep 2026 22:05:54 GMT

What happens between receiving a claim and resolving it?
A final status tells us whether a claim was paid or denied. It doesn’t show how many reviews it passed through, whether activities repeated, or whether a denial was followed by an appeal. This report uses Threadseer in Power BI to explore those journeys and connect process-level patterns to individual cases.

About the data
The sample contains 12,200 events across 1,880 claims, 12 activities, and 10 distinct process paths. All data is synthetic. The findings illustrate an investigation workflow; they do not represent an actual healthcare organization or industry benchmark.

1. The most common path accounts for less than a third of claims
The largest group (600 claims, or 31.9%) follows this sequence:

Claim Received → Eligibility Check → Coding Review → Adjudication → Payment Approved → Paid

The remaining 68.1% follow other routes. Those variations include additional reviews, a shorter payment path, requests for information, corrections, denials, and appeals. The most common route provides a useful comparison point, but a different route does not automatically indicate a problem. For example, 250 claims include Medical Review before continuing to payment, without repeating an activity.

2. More than one in four claims repeats an activity
Across the sample, 500 claims (26.6%) contain rework, identified by repeated activities within a case. Three paths account for these cases:

220 claims repeat Eligibility Check after Need More Info.
180 claims repeat Coding Review after Provider Correction.
100 claims return to Adjudication after a denial and Appeal Review.

These are different investigation targets. Information requests, coding corrections, and appeals should be examined separately because they may have different causes and operational requirements. Repetition alone does not establish that the work was unnecessary or preventable.

3. A denial is not always the final outcome
The sample includes 100 claims that are denied, pass through Appeal Review, and ultimately reach Paid. Another 280 claims end at Denied.

That distinction matters when interpreting outcomes. Counting every claim that encountered a denial would answer a different question from counting claims whose final recorded outcome was denial. Following the full sequence makes that difference visible.

From patterns to investigation
The eligibility and coding loops are useful starting points for a closer review. In a real setting, I would investigate what information prompted the return, whether it could have been available earlier, and how elapsed time compares with claims that did not repeat those activities. The event log identifies where to look; additional operational evidence is needed to explain why it happened.

To reproduce the exploration, open the attached PBIX in Power BI Desktop, select Analyze if prompted, and explore:

Process Map and Variants to compare the routes claims follow.
Rework to locate repeated activities.
Bottlenecks to investigate timing along those routes.
Cases to trace the individual sequences behind each pattern.

About Threadseer
I built Threadseer to bring interactive process mining into Power BI. Analysis runs inside the visual runtime without sending event data to an external service. This post uses the same synthetic sample shared in QuickViz, with a closer look at interpreting the claims workflow.

Find Threadseer on Microsoft Marketplace, or visit the getting-started guide to explore it with your own event data.

Veterans Affairs (Standardized Design.VA.gov)

KCoderVA — Thu, 17 Sep 2026 22:00:37 GMT

A Power BI theme aligned to the current U.S. Department of Veterans Affairs Design System (VADS), which builds on the U.S. Web Design System (USWDS). Colors are mapped to VADS semantic tokens, typography, palettes, and styles to allow reports to closely match modern VA.gov digital styling standards.

What's included: the full VADS-derived data color sequences, semantic good/neutral/bad mapping for conditional formatting, and per-visual defaults for cards, tables, matrices, slicers, KPIs, gauges, charts (bar, column, line, area, scatter, waterfall, pie/donut, treemap, funnel, ribbon, etc.), reference/trend/error lines, tooltips, and the filter pane. Titles, axes, gridlines, and legends are pre-styled defaults to ensure a clean, consistent look, with white canvas backgrounds and a very subtle tint on visuals to separate them from the page, while font sizing favors readability. Designed for VA analysts, programmers, designers, or employees who want VADS/USWDS-consistent Power BI reports.

Notes: Licensed under Apache-2.0. & based on the VADS color palette as published on https://design.va.gov. A colorblind-friendly companion theme is available separately in the Microsoft Fabric theme gallery.

FabCon and SQLCon Barcelona 2026 Badges

FabricMau — Thu, 17 Sep 2026 21:53:55 GMT

FabCon and SQLCon Barcelona 2026

Unlock Your Exclusive Conference Badge

Add the exclusive FabCon/SQLCon EU 2026 badge to your Fabric Community profile.

How to get the badge:

Make sure you are signed in to your Fabric Community account.
Like or comment on this post using the community profile where you would like to receive the badge.
Complete this action by October 5, 2026.

Badges will be added to eligible community profiles after the conference once participation has been reviewed.

Community Lounge hours

Tuesday, September 29
11:30 AM to 5:00 PM

Wednesday, September 30
9:00 AM to 4:00 PM

Thursday, October 1
9:00 AM to 2:00 PM

Explore the Community Lounge

During the conference, the Community Lounge will include:

Community theater sessions
Meetups and community conversations
Certification and learning conversations
The Giveback Activity
Game Hour and community activations
SQL Kicks raffles

Session and activity times may change. Please review the final schedule displayed in the Community Lounge and included in the official event materials.

Ready to participate?

Like or comment on this post while signed in to your Fabric Community profile by October 5, 2026.

Storing and parsing HL7 data

ipkus — Thu, 17 Sep 2026 21:38:05 GMT

Any healthcare folks can share what best practices they have found around storing HL7 data for analytics ?