When you are working on Power BI, one of the fundamental aspects of building a robust data model is, defining how different tables are related to each other. This relationship determines how filters and slicers affect the data, and one of the key settings you will encounter is the Cross Filter Direction. But what does this actually mean, and how does changing it impact your report’s behaviour?

I will walk you through in this blog the feature of Cross Filter Direction in Power BI. the types of filtering relationships available, the potential effects of changing these directions, and the advantages and considerations of each approach.

Ever wondered how to replicate SQL APPLY clauses in DAX ?

In the following article ,I propose a ingenious method to replicate the APPLY behavior in DAX.

Before the introduction of TMDL (Tabular Model Definition Language), modeling tasks in Power BI Desktop was almost entirely GUI-driven. Any advanced tabular modeling tasks required external tools like Tabular Editor, ALM Toolkit, or SQL Server Management Studio.

These tools are not created by Microsoft, many organizations do not allow them, and some require additional licensing. In this blog, let’s talk about a few tasks that the new TMDL view enables directly inside Power BI Desktop tasks that were not possible using the GUI at the time of writing.

Changing the Storage Mode

Before TMDL, switching a table from Import to DirectQuery was not possible in the GUI. TMDL makes it simple.

 

1. Open your semantic model.
2. Switch to the TMDL view.
3. Select the table in the Model Explorer on the right.

   

4. Click Script TMDL to → Script tab.

 

5. A new tab will open with the TMDL script for creating or replacing the table.

6. Scroll down to the partition definition and find the mode attribute

7. Change the value to the desired storage mode and click Apply.

That's it, the storage mode is now updated.

 

 

 

Note:

If you switch from DirectQuery to Import, remember to refresh the table so that data loads into the model.

You can use the same approach to switch to other modes like Dual.

Object-Level Security Implementation

Before TMDL, implementing OLS required Tabular Editor or SSMS. Now it can be done directly in Power BI Desktop.

Below is an example that creates a role called RestrictEmployeeId and hides the employeeId column in the Employee table:

Another example that hides the entire Employee table:

 

Write your own script based on your model and run it in the TMDL view.

 

You can see the created roles in Model Explorer.

 

Creation of Perspectives

If you have used the Personalized Visuals feature, you may already know what Perspectives are. They are used to provide a simplified, focused view of a model.

Earlier, creating perspectives required Tabular Editor. TMDL now supports it directly.

The example below creates a perspective called EmployeePerspective that includes selected columns from two tables:

Run a similar script based on your model in the TMDL view.

 

Your perspective will appear in Model Explorer.

 

Addition of Cultures and Translations

If you have created multi-language Power BI reports, you may already know about Cultures.

Previously, adding a new language or translation required Translation Builder or Tabular Editor. With TMDL, you can define cultures directly.

Example: Adding Spanish (es-ES) language and translating several objects:

 

Run the script in the TMDL view.

 

The culture will appear in Model Explorer.

 

 

Configuration of Detail Rows Expressions

If you build self-service datasets for Excel users, you may already know about Detail Rows. This property controls exactly which columns are returned when users drill through in Excel PivotTables connected to your Power BI semantic model.

Before TMDL, setting this property required Tabular Editor. Now it can be done in Desktop.

The example below creates a measure EmployeeCount and defines a Detail Rows expression for it:

 

Run this script in the TMDL view.

 

To validate the property, you can run a DAX query:

EVALUATE DETAILROWS([EmployeeCount])

 

Creating Custom Table Partitions

Before TMDL, splitting a table into custom partitions required Tabular Editor or other external tools. Now this can be done directly in Desktop.

Script your table into TMDL view, locate the partition section, and modify it as needed.

Example: Splitting the Employee table into two partitions:

 

Run the script in the TMDL view.

 

You will see the new partitions under the table in Model Explorer.

Note: After updating the partitions, refresh the data in the table.

I hope you find this information useful, If I missed any other TMDL capabilities that were not possible through the Power BI Desktop GUI, please share them with me.

Happy Learning!

Data chaos is one of the biggest hidden costs in any organization.
When your Power BI reports are slow, inaccurate, or difficult to maintain, the root cause is almost always the same: a weak or inconsistent data model.
This article shows you how to transform scattered operational data into a clean, scalable, high-performance Power BI data model the same principles used in real-world global operations.

Welcome to Our November 2025 Newsletter!

We’re excited to bring you fresh insights this month as we begin a brand-new series on Scenario Analysis in Power BI.

Starting with this edition, I’ll be sharing a collection of articles that explore different approaches to scenario analysis and their practical applications in real business cases.

In this first article, we’ll focus on Simple Scenario Analysis — integrating What-If Parameters to dynamically adjust key variables and visualize how changes impact outcomes directly within Power BI.

Resources and files for this article are available for download here: https://1drv.ms/f/c/357fb5c8090fe1b2/EraKe-FIlR1EpTL1HYTpAREBYFALs9GKTMh7MnWebL6mWg?e=05Qg4J

In this analysis, let’s create a group (using Home → Enter Data) to organize all the measures we’ll be using.

Have you ever tried explaining a Power BI issue and immediately realized you can’t share the dataset behind it?

Maybe the data is confidential, maybe it’s messy, or maybe you just don’t want to hand-craft a new example every time. Before you know it, you’re exchanging files, fixing paths, and losing momentum...

My solution? Generate a sample dataset using Power Query only!

Many Power BI reports slow down because of date handling mistakes. This post explains why auto date/time hurts performance and how to build one proper calendar table for faster, accurate reports.

While developing a BI report, sometimes information is spread across multiple columns and other times it is packed into rows. There are three powerful Power Query transformations come into the picture, which are Pivot, Unpivot and Transpose.

In this blog, l will walk you through what they are, why they are useful and how they different.

In this aricle we delve into how to use additional column context with TimeIntelligence functions of DAX.

Wanting some accountability for studying for your Fabric certification exam.  Read on for details on how to join a study group.

When you start building a report on Power BI platform, you can quickly see lines connecting tables in the Model View. It looks very simple but behind those lines lies the secret of how Power BI understands your data. What do these actually mean?

I've launched a new open-source project, to analyse the usage and dependencies between Power BI objects. This is very handy as a quick reference to the connections between Reports, Pages, Visuals, Tables, Fields and Measures in any Power BI solution. The analysis data is generated using the Measure Killer application.

The Translytical Task Flow feature marks a significant evolution in Power BI, From a read-only analytics tool to an interactive, action-oriented platform. By combining Fabric UDFs with new UI elements like Text and List Slicers, you can now build seamless workflows that empower users to act on insights instantly.

If you are exploring Power BI, you might already have come across the Drill Down and Drill Through options. At first, both look similar in this way they help you explore data in detail. But in real time, they work differently and are used for different purposes.

As a Fabric User, I've witnessed the evolution of data platforms. We've all managed the chaos: a data lake here, a data warehouse there, and a labyrinth of ETL pipelines connecting them. It's complex, costly, and slows us down.

Microsoft Fabric introduces a feature that fundamentally changes this paradigm: OneLake.