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 GA, 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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Many organizations have long depended on on-premise databases built on relational systems to manage their data. These setups have been crucial for structured data handling and analysis, offering stability and reliability. However, as data demands increase both in volume and complexity, more scalable, efficient, and cost-effective solutions are needed.
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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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Need to connect Tableau to a dataset or semantic model in PowerBI(now Fabric)?It is not as difficult as you may think.
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In Power BI, you often has to decide whether to create a column or measure to achieve your analytic goals, though they can be used inter-changeablely in a many cases, they are very different in how they function and perform.
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It happens sometimes that the report metrics usage report suddenly stopped refreshing, you waited 24 hours as suggested and click on that refresh button hundreds of times and still no new data, then you tried to check the semantic model, it gives this sad message.
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