Power BI slicers are powerful tools for making data more interactive and customizable within reports. They allow users to filter visuals on the dashboard by selecting specific criteria, making it easy to narrow down data for quick insights. In this article, we’ll dive into what Power BI slicers are, how to set them up, and how to sync them across multiple pages to create a seamless user experience.
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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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Geographic data is often stored in a Degree-Minute-Second (DMS) format, making it a bit tricky to convert into the standard decimal degrees (DD) required for Tableau’s mapping functions. In this post, I’ll walk you through how to transform DMS coordinates to decimal degrees directly in Tableau, step-by-step, so you can plot your geographic data without headaches.
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In this post, I’ll explain how to add the current date and time to an existing PySpark DataFrame in a Fabric Notebook. This is particularly helpful when inserting data into a Fabric Lakehouse table, as it allows you to track when each record was added. I’ll walk through an example using a DataFrame to first load some sample data and show how to append a new column with the current timestamp to capture the insertion time.
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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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Be sure to check out my previous article on creating a Likert scale, as we’ll be using the same restaurant survey dataset. However, we won’t be using any of the calculated fields from the Likert scale, so feel free to skip it if you’d like. That said, the final dashboard will feature both the Likert and NPS charts side by side—they always pair well together, don’t you think?
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Surveys are powerful tools for gathering feedback and insights, with Likert scales and Net Promoter Score (NPS) being two popular methods for measuring attitudes, opinions, and customer satisfaction. Likert scales assess the level of agreement or disagreement on a statement, while NPS measures customer loyalty by asking how likely respondents are to recommend a product or service.
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In the world of databases, SQL is a foundational language that serves as the backbone for interacting with relational databases. However, when delving deeper into SQL, you will come across terms like ANSI SQL and T-SQL, which may seem confusing at first. In this article, we’ll explore what SQL, ANSI SQL, and T-SQL are, how they differ, and their specific use cases.
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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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