Scenario:  

In the use of Power BI's built-in visual objects, some users wish to annotate certain values in line charts. However, Power BI's built-in visual objects do not provide this functionality. As a workaround, we can use Python to create a line chart and annotate some special data points in it. Let's take a look at the example below.  

How to compare data in different date ranges.

Scenario:
In some cases, we may need to split string fields into columns based on specific conditions. The purpose of this article is to illustrate how this can be accomplished using M functions for a specific case.

Let's take a moment to chat about a Power Bi external tool. For some time now, there are several well-known tools that we use more and more every day to support our developments in Power Bi Desktop and beyond. This tool, in particular, is gaining popularity for "data modeling cleanup," but we can use it for other purposes as well.

This article introduces the tool Measure Killer so we can learn about its main free features and analyze if we want it to accompany us like many others

Power BI Desktop empowers users to craft visually compelling dashboards and reports adaptable for both desktop and mobile interfaces. Its intuitive interface facilitates seamless transitions between designing for desktop and mobile platforms, guaranteeing optimal viewing experiences across various devices.

Dynamic rollback of the previous N weeks of data

Discover a simple, DAX-free method to highlight maximum and minimum data points in your Power BI visuals, enhancing insights effortlessly through conditional formatting.

In Power BI, we will encounter the many-to-many relationships frequently. Typically, when faced with this situation, we always create a bridge table to establish connections and process the data. However, creating a bridge table will involve the calculation of more than three tables, and it may lead to circular dependencies sometimes. As an alternative, in certain scenarios, we can explore using the Cartesian Product to handle the data. Let's look at a sample in the next sections.  

Scenario:
In some cases, we may need to construct a table that contains only those entries that satisfy certain filtering criteria. The purpose of this article is to illustrate how to use the Dax or M function to achieve this function.

Discover how to automatically update your Power BI date filters including Date Slicers and visuals to the latest date from your fact table.

Check your data for issues and anomalies in your tables. Make it easier for everyone to understand by adding a data profiling report tab in your Power BI report. I'll guide you through creating one using simple steps, including how to use the GUI data profiling and Table.Profile function for more insights.

Scenario: 

In this post, tooltip and measure are utilized to show the breakdown of data for different classifications. 

In this blog, I'll show you how to use Lakehouse's SQL analytics for efficient data warehousing. I'll guide you through the process with practical examples, making it easy to ingest data into Lakehouse and access it in Fabric Datawarehouse.

The world of DAX is on fire with the release of new functions at the end of 2022. Many posts on various blogs have appeared showing many scenarios explaining the new functions.

Most articles try to explain the functions. This article will overlook that and will try to compare how time intelligence functions respond with traditional code compared to the new DAX functions to solve those classic date scenarios.

Scenario: 

Sometimes when we create a report, we may want to implement a complex data sorting in visuals according to multiple ranking rules. This is not easily done by sorting by a single column or ranking by a single expression. We will need some DAX techniques to get there.

In this article, we want to sort car brands in a table visual according to several rules. And when a ranking is selected, a dynamic display includes the two car brands prior to the selected ranking.