Learn the difference and what it takes to transition to Fabric successfully!

In this article, we’ll explore the power of X-Functions like SUMX, AVERAGEX, and more, and how they can transform your data modeling in Power BI. Let’s clarify what we mean and dive into practical examples to enhance your analytics workflows.

Downloadble Resources 

How the Text Slicer Works
The text slicer enables the user to input his specific text. He then specifies which particular data field would be filtered. As soon as one types the text in the slicer's input box, the information will automatically reduce to showing only those data where the text has been located. It is beneficial when dealing with huge lists because speed and accuracy must be delivered quickly on the filter.

What are Small Multiples?
Multiple instances of the same visual, side-by-side in different dimensions, are known as small multiples. This helps compare data across many segments at a glance. For instance, you can split a sales performance card visual by region, product category, or time to give a holistic view of your data.

Managing stock levels dynamically is essential for effective inventory management. A crucial aspect of this task involves calculating cumulative quantities to track the balance of products over time. In this example, we have Sales and Purchasing tables, and we aim to create a Matrix visual in Power BI to display the purchased and sold quantities accurately. The ultimate goal is to dynamically track the stock balance (leftover products) at any given point by calculating the difference between cumulative purchases and cumulative sales.

You can download the files related to this article using the following link:
Materials 

This article explores the concept of data lineage in Power BI DAX calculations, providing a detailed case study on calculating monthly sales percentages. Learn how to address common sorting issues and ensure accurate data representation by understanding and managing lineage in your DAX measures.

YouTube video in English | YouTube video in Russian | Article in Russian

From time to time I challenge myself to imitate creative vizualizations I stumble upon on the vast expanse of Internet using only standard visuals in Power BI. So this article is all about one of those challenges.

Having found the chart below, I started thinking how one can create a pie chart where slices differ not only by sector width but by its length as well, if it is allowed to put it like that.

Source of inspiration

And I did find a way. 

Final result

Recently, I came across a requirement where I had to display hierarchy data. Lower levels of data should be shown as a ratio of all the segments available rather than a ratio in its own group. Also, some multiplication factors need to be applied. In addition, hierarchy needs to be summed up from lower levels instead of grouping and rolling up. Below article describes how set up the calculation and data.

I've created a python notebook that helps to automate review and testing of Power BI reports. It embeds a report and then works through every (visible) page and exports the data from every (visible and exportable) visual.

In this newsletter, we’ll dive into hierarchical directories and how they’re used in analytics. Let's clarify what we mean.

Downloadable Materials 

Power BI can connect to, transform and visualise data. Which is great but how do we get that modified data out?

One way is using a Power Automate flow to connect to the published Power BI dataset.

In this video we discuss how to manage a notebook PySpark code throughout the deployment pipeline stages, so that it adjusts dynamically to each deployment stage without needing manual interaction.

All About Power BI Metrics, A hidden Gem

It's a great external tool to generate developer documentation for our reports with a single click, and we want to have a report or document that documents the entire process from data import to Power BI, and from Power BI report authoring to delivery.

In the process, we'll have a lot of pages, measures, parameters, calculation groups, even Power Query queries, and so on. Sometimes we need something to be able to visually observe it and count this information, so the tool shared today will be able to help you!

Custom Sorting with Power BI

Custom sorting in Power BI can significantly enhance the clarity and impact of your data visualizations. However, users often encounter several challenges when setting up custom sorts. Here’s a look at some of the common hurdles and how to overcome them:

Step 1

Create a Custom Table with all the category either manually in Power Query or read from source.

Create a new column beside and provide the number in the order you want to arrange.

Step 2

Create a relationship between the column in actual table to the custom sort table.

Step 3

Create a column in Actual table with an expression.

Sort order = RELATED(CustomSortTable[Sort])

Step 4

Select the column you want to sort, and a “Column Tools” menu bar will appear at the top. Now Select the “Sort by column” and select the sort order column.