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Posts Tagged "Tabular"

Analysis Services Tabular: Displaying History and Slowly Changing Dimensions

Matthew Gajdosik

Historical reporting is common enough, but what are some ways to slice through your historical data in SQL Server Analysis Services (SSAS) Tabular? Tracking and including historical data or Slowly Changing Dimensions (SCDs) is common enough in data warehousing, and Business Intelligence as a whole, but putting it into an easily-digested form is always a new set of issues.
In this post, I will walk through some strategies we’ve used for integrating historical data into reporting and analytics solutions with SSAS Tabular, as well as some ways you can restrict this information to give your users a cleaner experience.

Analysis Services Tabular: Many-To-Many Relationships, Bridge Tables, and Blank Members

Matthew Gajdosik

With bidirectional filtering in SQL Server Analysis Services (SSAS) Tabular 2016, it’s easier than ever to build many-to-many relationships into your model. But what are some ways to avoid trouble when building them? This post covers two topics: (1) a scenario that can cause Tabular to match completely unrelated groups across a many-to-many relationship, and (2) some strategies for automating your bridge tables (including where they might need some brains!). If you’re already comfortable with this topic, and just want to see some DAX, feel free to skip right to the calculated bridge tables.
Otherwise, let’s say you’ve built up your many-to-many relationship based on a bridge or relationship table someone generated in the database, and have measures based on one or both of the tables connected by the bridge. When looking at the results, you see that a number of the results…

Analysis Services Tabular: Factless Facts and Revealing Object Relationships

Matthew Gajdosik

Defining relationships is central to data analytics, but how can you use SQL Server Analysis Services (SSAS) Tabular to keep them from being overlooked? Often, you can run into the situation where you really just need to say what is missing from a data set or report. Usually, you can work around this conflict of expectations versus reality by building a factless fact. This type of fact table is really just a list of relationships, where the “fact” is really the existence of the row.
Typically, this type of table is built directly into a data warehouse, and helps to materialize calculations that would otherwise be too expensive or awkward to calculate. Think about the steps that you might need to take in order to highlight gaps in data (such as a student missing class), if you were not provided a tidy checklist of…

SQL Server Analysis Services Multidimensional vs Tabular

Mark Frawley

It is no secret that Microsoft is moving away from the original “Multidimensional” (MD) flavor of Analysis Services, instead focusing all efforts on the new “Tabular” (Tab) flavor.  This drives the standard advice that Tab should be the basis of most new cube projects.  Another idea driving this is that for 60-70% of projects either technology likely will work fine – so it is prudent to go with the newer one which is much earlier in its lifecycle.
However, there are many differences between the two technologies.  In particular cases, one or the other is clearly the best fit, but without understanding these differences it is difficult to even assess whether your project is in the 60-70% where it doesn’t matter, or the 30-40% where it may.  Even if you would elect Tabular regardless to stay earlier in the product lifecycle, you should be sure you understand the tradeoffs.
Since MD is…