![]() It is therefore obvious from the previous screenshot that Availability stock occupies nearly 31GB and of which 22.5GB is a dictionary. Table size – Columns size + User Hierarchies Size + Relationship SizeĬolumns total size – Data size in columns (Data size, Dictionary size, Hierarchies Size)ĭictionary size – the size of a dictionary related to compression details about compression can be found in the article from Albert and Marc here Meaning of selected columns from screenshot:Ĭardinality – number of rows in the table, number of unique values in the column These two factual tables took 88.76% of the total size. We keep a longer history in the data warehouse. The facts are in the last 3 fiscal years, so we now have about 2.5 years of data. (1.8 billion large stock snapshot table). Most places took the biggest two fact tables.ģ1 GB availability stock (539 million large table entries related to goods availability) and 27.3 GB stock Total database size 65.6GB without any optimization. Measurement results without any optimization Just change the connection string to your SSAS and update the data. It can be used for both Tabula and Power BI analysis. Basically it is a Power Pivot model built over the SSAS metadata. ![]() The tool can be downloaded here including tutorial. I used Vertipaq Analyzer from SQLBI to analyze the size ( ) I missed some of the little ones because the exercise would take too much time and the effect on size would be minimal anyway. I created a model containing all Measure Groups. If I wanted to redo the existing solution to Tabula, how much memory does the model take, in which I have the same data available? ![]() It takes up to 86 GB of disk space on MOLAP storage. I have a multidimensional cube of about 11 measure groups, 27 dimensions. So you can look forward to follow-up blog posts. (if my requests are not answered, nothing else is left for me).īut it will not be a day to remake something that has been created for about 4 years. In summary, I am starting to think strongly about redesigning an existing multidimensional solution at Tabular So I wonder if I will write Czech, alternate languages, or go to English (to better influence product development). “If you want to change something, it must reach the product team.” I’ll make it a separate blog post in English, because one wise man told me about my MVP activities. What I am waiting for in Multidimensional and starting to become a real pain are session level calculations at the report level for live connection. Tabular did not meet all the functional requirements, but after a few years the situation is a bit different. Multidimensional was chosen for existing solutions in the company mainly with regard to function. (we have not seen breakthrough news in Multidimensional since 2008) and support from client applications, especially Power BI, is lagging behind.Īnd that’s what makes me the most angry lately and reevaluating whether sticking to multidimensional is a good idea (and I’m a big fan). There is a future in the Tabularu, Multidimensional is no longer developing The priorities of the Analysis Services development team are clear. There will be a second edition, wait for a complete overview. Specifically, the lecture’s description of the engine is based on a book by Albert Ferrari and Marc Russ: Ask yourself what your servers have more:īut is it really necessary to worry about the lack of memory? Tabular and Power BI compresses data into memory.Īmong other things, how the compression in Vertipaq repository works I was talking on WUG Days and you can watch it here Multidimensional holds data on disk, Tabular in memory. One of the factors entering decision making is storage architecture. There’s no quick answer (and maybe a separate blog post). The relatively frequent question I receive at conferences and training on analytical services is: When to use Multidimensional, when to use Tabular (Power BI runs on Tabula). ![]() Power BI users, hold on, the article will be relevant to you □ Maybe □
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