Regular Expressions, or Regex, are not unique to the Business Intelligence (BI) world, but they are something I frequently encounter and often find puzzling. I wish I had a handy cheat sheet whenever I need to use Regex in my formulas, but I can never find a concise and helpful one through Google searches. So, I decided to create one for people like me who don’t necessarily struggle with Regex but are often annoyed by its complexity.
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I recently took Microsoft’s DP-600 exam/Fabric Analytics Engineer Associate(Twice 1 fail and 1 pass), and I noticed that several questions covered Power BI External tools, such as DAX Studio, and the correct use of DAX statements. Therefore, I think I ought to write a post on DAX studio for those of you who are also interested in taking this test.
In today’s data-driven world, organizations are increasingly relying on robust data infrastructure to manage and analyze vast amounts of information. Microsoft Fabric provides comprehensive solutions to address these needs, including data warehouses and data lakehouses. Understanding the differences between these two approaches is crucial for leveraging their capabilities effectively. In this blog, we’ll dive into the distinctions between data warehouses and data lakehouses within the Microsoft Fabric ecosystem.
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Before diving into the process, it’s important to understand what a shortcut in OneLake entails. Essentially, a shortcut in this context is a reference or link to a dataset or specific data object within OneLake. This allows users to access data quickly without navigating through multiple layers of the data lake’s structure, facilitating faster decision-making and smoother workflows.
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Power BI is consistently expanding its footprint in the enterprise environment. One lesser-known but highly valuable feature is the capacity to certify datasets, which enables organizations to signal to end users that certain datasets are more reliable. We are going to explore this valuable yet often overlooked feature, and to discuss the difference of promoted and certified dataset and use cases of when to use which.
A lot new terms come out Microsoft Fabric went G, one of them is direct lake. When you hear that for the first time, you may have the same response as me :”ain’t that the same thing as direct query” well, no, not really.
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Following our discussions on Fixed and Exclude LODs in earlier articles, you should now be fairly comfortable with these concepts. Therefore, tackling Include LOD should be relatively straightforward. Let’s dive in without further ado.
In a prior article, I illustrated and demonstrated the process of converting Tableau’s fixed level of detail (LOD) to Power BI. In this piece, I will extend this effort by guiding you through the translation of the EXCLUDE LOD to Power BI DAX.
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If you’ve navigated Tableau’s powerful Level of Detail (LOD) function, you know its complexity and potential for creating impactful visualizations. Now, whether you’re transitioning from Tableau to Power BI or need to excel in both platforms with limited Power BI skills, fear not! In this series, we’ll unravel the mysteries of applying Tableau’s three main LOD functions in Power BI.
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There are two types of measures in Microsoft Power BI models: implicit and explicit. Implicit measures are automatic and summarize column data in visuals.
Explicit measures, also referred to as just measures, are custom calculations you create for your model. Put simply, implicit measures are columns that can be aggregated automatically, while explicit measures are custom calculations defined using DAX, and is THE measure that we are familiar of.
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