Power BI is a powerful tool for data visualization, and often, users integrate background images, icons, and visuals to create custom dashboards that elevate the storytelling behind the data. But what if you need to extract these images from a PBIX file? While Power BI Desktop doesn’t offer a built-in method for image extraction, there’s a clever trick that allows you to extract embedded images and backgrounds.
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Power BI is a powerful tool for data visualization and analysis, but navigating through its various features and functionalities can sometimes be time-consuming. To enhance your productivity and make your workflow more efficient, Power BI offers several keyboard shortcuts. Whether you’re cleaning data, building visualizations, or customizing reports, these shortcuts can save you time and effort.
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If you’ve read my previous tutorial on creating Likert Scale and NPS scores in Tableau, you might have found yourself wondering: how can I achieve the same in Power BI? The good news is, Power BI offers a straightforward way to replicate this process while leveraging its powerful visualization and data modeling features.
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Power Query is a powerful tool used in Power BI, Excel, and other Microsoft products to clean, transform, and load data. While it simplifies data preparation, users often encounter various errors that can disrupt workflows and hinder progress. Understanding these errors and knowing how to resolve them is crucial for any data professional. In this blog post, we will explore some common Power Query errors, their causes, and practical solutions to fix them.
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In today’s fast-paced business environment, efficiency and productivity are paramount. Microsoft Fabric, a robust and versatile platform, has introduced a new feature aimed at enhancing workflow management: Task Flow. This feature promises to streamline processes, reduce manual intervention, and ultimately, boost productivity. In this comprehensive guide, we will explore the ins and outs of Task Flow, its benefits, and how you can leverage it to optimize your operations.
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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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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.
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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