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Awesome video, Thanks Mellisa. This is a very well paced and clearly explained demonstration of your creative approach. YOur eaching style is the best to bring home the key point in your approaches. 👍
Hello Sir, Could you upload your courses on M Language on Udemy please 🙏 !!! Your DAX course, I bought and learnt everything through it but M Query is still missing !! Students like me are waiting for the release of M Query Course from Enterprise DNA on Udemy
interesting. but how do you handle this when you have 2 fact table? I have 2 fact tables Fact_Sales Fact_Promo 2 conforming dims Dim_Customer Dim_Date Not all customers in Fact_Sales are in Fact_Promo Therefore if I want to see all sales by customers in Fact_Promo, this measure will work: VAR _Result = CALCULATE( [Total Sales] , CROSSFILTER ( dim_Customer[PK_Cust_ID] , Fact_Promo[PK_Cust_ID] , Both ) ) RETURN _Result What I really need is all sales for each customer in Fact_Promo where: Fact_Sales[Date_Order] is between Fact_Promo[Date_Start] and Fact_Promo[Date_End]
If your data doesn't include the inventory's Cost Per Item, would I be right in assuming you could use the Item Count just as effectively for your Stock Ratio, maybe not for when you should discount slow movers, but for alerting you that you should be sure you've ordered the fast movers?
Have a question on this, would the junk dimension normally be used across multiple facts? Or would it be more common to have a junk dimension for each of the facts that could use one? If we make one that is used my multiple facts, the columns we can actually have in it are very few. But by having junk dims for each of the facts we can have several columns in each of them. Thoughts?
Is there a way to format the Expenses as positive without affecting the Grand Total? The assumption being that, since they're already in the Expenses section, they already represent money going out, so the minus sign or parentheses are not needed.
That's wonderful. I am also working on a Gantt chart visual using Matrix which is showing data on an hourly basis. And I am implementing the same logic to get the length of data bars varying over the duration between Start time and end time, however I'm not successfully able to format it and data bars length are not appearing as expected. Could someone please help me with this?
Thanks for the video. Very well laid out. Have you or anyone else figured out a way to do this with one measure? It would be nice to have a dynamic way to switch between dates with one measure.
Then my question is, how the hell do you even have these dates for which you have no sales revenue data in your model? Most people like me, want to make forecasts for data they don't have. In other words, the final date for which I have data in my model is 31-12-2024, and I want to make forecasts for 01-01-2025-31-12-2025. BUT, these dates are not in my model as rows, which is the case in YOUR example.
will this forecast auto refresh after the new data is added in published PBI service? since the python code runs in local env. Do I need to run this locally and update the dashboard?
If auto-refresh is important, consider moving the Python process to an external ETL pipeline that updates your data source. Then, you can set the dataset to refresh in the Power BI Service automatically.
