![]() ![]() I’m now going to discuss the KPI functionality in the Tabular model that was introduced in SQL Server 2012. For those of you who are unfamiliar with the KPI functionality in the Multidimensional model, the blog post can be found here. Microsoft’s whole purpose is to empower Non IT organizations by providing them more and more user friendly Business Intelligence solutions so somewhere they are trying to remove the complexities of implementation.A few weeks ago, I wrote a blog post that discusses implementing KPIs in SQL Server Analysis Services Multidimensional. In the long run, tabular models are easier to develop and easier to manage. ![]() Tabular is similar to relational model and it has columns and rows while multidimensional is as name suggests it has multiple dimensions. If you want multidimensional models in the cloud, the only way is to deploy SQL Server Analysis Services in Multidimensional mode to an Azure VM.īut if we talk about the future, Microsoft will provide everything into Tabular Modeling as Azure Analysis Services supports only Tabular Models. Multidimensional models will not be supported in Azure Analysis Services or Power BI Premium datasets. Tabular Models we can create on SQL Server Analysis Services and also on Azure Analysis Services while Multi dimensional models we can only create on SQL Server Analysis Services. Tabular databases can sometimes get greater amounts of compression, about one tenth the size, especially if most of the data is imported from fact tables. Primary storage of a multidimensional database will be about one third size of the original data. ![]() The Tabular model reads data directly from the cache and takes advantage of the query acceleration resulting from the column store indexes, while the Multidimensional model is reading pre-aggregated data or atomic data from disk depending on the existing aggregations defined during the design of the cube. In Tabular databases, the hard disks are not as important, but the amount of RAM and CPU speed is very important. The CPU core speed is also very important for Tabular databases. If you do not have enough memory, the Tabular model will simply fail. ![]() It is important to clarify, that the hardware used for Multidimensional databases in many circumstances cannot be used in the Tabular model. When you have simple requirements and you need to collect data from multiple different sources then Tabular modeling is better approach as it provides more support to the sources. The creation of bi-directional filtering is for filtering through one table, say a fact table, to an aggregation in a dimension table. Bi-directional filtering is used for many-to-many. The Tabular Model of Analysis Services uses a new feature in SQL Server 2016 called bi-directional filtering. SQL SERVER 2012 in which Tabular model got introduced at that time it did not have Many to many relationship support but there was a way around to achieve many to many relationships using DAX. But, if your requirements have lot of many to many relationships to build then multi dimensional approach is better. Suppose, you have very simple requirements that can be covered with relational approach itself then Tabular modeling is worth to answer your business analytical questions. Sometimes multi dimensional approach is the solution to fulfill all the business requirements and for few requirements Tabular modeling is only the solution. we need to understand that it all depends on business requirements. Here, we can compare differences between multi dimensional approach and tabular modeling approach but one is not better than the other. SQL Server Analysis Services (SSAS) provides several approaches, or modes, for creating business intelligence semantic models: Tabular and Multidimensional. ![]()
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