![]() ![]() That is about 8,009 years, which makes almost 3 Million rows in the default date table. The minimum value in that column is 1st of Jan 1990, and the maximum value is 31st of Dec 9999! Wow, the maximum value changes everything isn’t? This means that in the date dimension we will have all days (one day per row) from 1st of Jan 1990 to 31st of Dec 9999. Values in that date table would be from the minimum date value in that field (column) to the maximum date value, with one row for each day. Which we call it the default date dimension table. This now means that the Date field in our sample table, is not just a field, but it is a table. Power BI creates a default date dimension for every single date field in your dataset. There are a few parts of the previous paragraph that you need to read more carefully: Well, this was a very quick wrap of the article below (which you can read to get a more in-depth understanding of it) However, having the default date dimension will make many of date calculations simpler and easier, and that is why many people are using it that way. There is an option to disable the default date dimension if you want and create your own date dimension. For understanding the current behavior mentioned in this article, you need to know what is the default date dimension and how it works. I have previously written about the default date dimension in Power BI and the difference of that with a custom date dimension. taking 150MB runtime memory! This is considering that we have only three distinct values in the column! Seems a bit strange, isn’t? let’s dig into the reason more in deep. I opened the file above in Power BI Helper, and in the Modeling Advise tab, this is what I see:Īs you can see in the above output, the Date field in the Date table is the biggest column in this dataset. You can download Power BI Helper for free from here. ![]() If you have a large *.pbix file, you can investigate what are the columns and tables that causing the highest storage consumption, using Power BI Helper. Now let’s see why this happens and how to fix it. You can try it yourself, and you would probably get the same experience. However, when I save the *.pbix file, the size of the file seems a bit crazy! This is not a big table at all as you can see. ![]() I have a sample dataset, with one table, and three rows! as below If you like to learn more about Power BI, read the Power BI book from Rookie to Rock Star. I have run into this many times in my consulting gigs with clients, However, a friend of mine Rui Romano’s ( B| T) great presentation made me think that there are still many people who are not aware of this, and need guidance on it, so time to write about it then. Sometimes, your model size grows significantly big, and you have no idea why!? In this article, I will show you one of the performance issues which might cause because of specific date values, and how to fix it. If you are interested in ordering food from The Boozy Bakery Kitchen for carryout or curbside delivery, please visit The Boozy Bakery at and click on the Boozy Bakery Tab.I have written about Date Dimension in Power BI in many posts, and it makes sense now to explain one of the most common performance challenges that I see in some of the models. | RELATED: Across the Table with Tom Slattery >Īnd don’t forget dessert from The Boozy Bakery! Check out The Boozy Bakery™ and JJ’s Wine, Spirits & Cigars online to see what is available for take-out, or to have right in JJ’s Bar! you can find out more about their specials and events, by checking out their website at. They’re located at 3000 West 57th Street in Sioux Falls. Cody Ingle and Ashley Thompson at JJ’s Wine, Spirits & Cigars!Īt JJ’s they like to say they provide “an enhanced adult beverage experience” and we think that’s just perfect to sum up the great food and drink and the great people you’ll run into at JJ’s Wine, Spirits and Cigars. ![]()
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