Power BI Insights: Geospatial data; Asymmetrical columns; Calculation Groups

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Power BI pros share tips on processing geospatial data, asymmetrical columns, and Calculation Groups.

Analyzing geospatial data with Data Explorer, Dynamic M and Power BI

Chris Webb took a look at how to analyze geospatial data with Power BI, Dynamic M, and Azure Data Explorer. In his example, he imagined a scenario in which a chain of supermarkets tries to determine what other stores are close to its location. Starting with Retail Points data from Geolytix, Webb loaded a CSV file into ADX and put together a  KQL query to filter the list of supermarkets. He then created two M parameters called selectedradius and selectedstore binding them to two other tables in the dataset with dynamic M parameters.

For now, not many people are using Azure Data Explorer as a data source for Power BI, but the approach offers a different way to demo the power of dynamic M parameters.

Creating a matrix with asymmetrical columns and rows in Power BI

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