DAX Tabular Model

Why Tabular?

SQL Server Analysis Services is a solid and mature platform that now serves as the foundation for two different implementations.  Multidimensional models are especially suited for large volumes of dimensionally-structured data that have additive measure values that sum-up along related dimensional attributes & hierarchies.  
By design, tabular architecture is more flexible than multidimensional in a number of scenarios.  Tabular also works well with dimensional data structures but also works well in cases where the structure of the data doesn’t resemble a traditional star or snowflake of fact and dimension tables.  When I started using PowerPivot and tabular SSAS projects, I insisted on transforming data into star schemas like I’ve always done before building a cube.  In many cases, I still do because it’s easier to design a predictable model that performs well and is easy for users to navigate.  A dimensional model has order and disciple however, the data is not always shaped this way and it can take a lot of effort to force it into that structure.
Tabular is fast for not only additive, hierarchal structured data but in many cases, it works well with normalized and flattened data as long as all the data fits into memory and the model is designed to support simple relationships and calculations that take advantage of the function engine and VertiPaq compression and query engine.  It’s actually pretty easy to make tabular do silly, inefficient things but it’s also not very hard to make it work really well, either.
tabular models can be faster and easier to design and deploy, and that they concisely perform well without giving them a lot of extra attention for tuning and optimization.  Honestly, there isn’t that much to maintain and a lot of the tricks we use to make cubes perform better (like measure group partitioning, aggregation design, strategic aggregation storage, usage-base optimization, proactive caching and cache-warming queries) are simply unnecessary. Most of these options don’t really exist in the tabular world.  We do have partitions in tabular models but they’re really just for ease of design.

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